a water quality assessment of milltown lake (muckno mill ...€¦ · ec european commission epa...
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A Water Quality Assessment of Milltown
Lake (Muckno Mill) Catchment, Co.
Monaghan using Macrophyte and
Macroinvertebrate Biological Indicators
By Caroline A. Wynne, BAgr.Sc. (Hons)
A thesis presented to the School of Health and Science, Dundalk
Institute of Technology in fulfilment of the requirements of the
degree of PhD
February 2012
Abstract
The fundamental aim of this study was to evaluate macrophytes as biological indicators in a
lowland agricultural catchment and determine whether they could reliably detect degradation,
both spatially and temporally. The objectives were addressed by assessing the relationship
between the macrophyte community and environmental parameters and by comparison with
an established bioindicator group in Ireland, the macroinvertebrates. Analysis of the aquatic
macrophyte community suggested that significant variation (35.8 %) was explained by the
physico-chemical parameters of the water column. However, physical characteristics at the
sites related to increasing size and habitat heterogeneity were also found to be significant.
Macrophyte and macroinvertebrate water quality indices were found to correlate with Total
Reactive Phosphorus (TRP). This suggests the potential of these indices to reflect water
quality in the catchment. Individual assessment of a macrophyte water quality index, the Mean
Trophic Rank (MTR) showed that at sites with slopes greater than 40 m/km the index did not
reflect water quality. This was related to poorly described reference conditions for that river
type. This highlights the importance of adequately described reference conditions to the index.
As reference conditions for sites in the drumlin landscape may be particularly difficult to
describe due to the impacts on water quality of the intensive agricultural practices in the area
as a whole, this may limit the usefulness of the MTR in this region.
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Table of Contents
Chapter 1 Introduction ............................................................................................... 1 1.1 Background to the Study ..................................................................................... 1
1.1.1 Surface Water Quality in Ireland ................................................................ 1
1.1.2 European Context ........................................................................................ 3
1.2 The Macrophyte Flora in Freshwater Biomonitoring ......................................... 4
1.3 Physico-chemical Parameters of Water Quality ................................................. 6
1.4 Interactions between Macrophytes and Stream Bed Sediments ......................... 9
1.5 The Macroinvertebrate Fauna in Freshwater Biomonitoring ............................ 12
1.6 Community Concordance Analyses .................................................................. 14
1.7 Water Quality Indices based on Macrophytes and Macroinvertebrates ........... 16
1.8 Aims of the Current Research ........................................................................... 20
1.8.1 The Macrophyte Flora of the Milltown Lake Catchment (Chapter 3) ...... 20
1.8.2 Characteristics of Stream Bed Sediments in the Milltown Lake Catchment
(Chapter 4) ................................................................................................................ 20
1.8.3 Community Concordance Analyses between the Macrophyte Flora and
Macroinvertebrate Fauna of the Milltown Lake Catchment (Chapter 5) ................. 21
1.8.4 Assessment of the Biological Water Quality Indicators used in the
Milltown Lake Catchment (Chapter 6) ..................................................................... 21
1.8.5 Assessment of Spatial Drivers of Water Quality in the Milltown Lake
catchment (Chapter 7) ............................................................................................... 22
Chapter 2 Site Description of the Milltown Lake Catchment ............................... 23 2.1 Milltown Lake Catchment ................................................................................ 23
2.2 Sampling Sites .................................................................................................. 29
Chapter 3 The Macrophyte Flora of Milltown Lake Catchment ......................... 38 3.1 Introduction ....................................................................................................... 38
3.2 Aims .................................................................................................................. 41
3.3 Methods ............................................................................................................. 42
3.3.1 Macrophyte Sampling and Analysis Methodology ................................... 42
3.3.2 Physico-chemical Sampling and Analysis Methodology ......................... 43
3.3.3 Statistical Analysis .................................................................................... 45
3.4 Results ............................................................................................................... 48
3.4.1 The Macrophyte Flora of the Milltown Lake Catchment ......................... 48
3.4.2 Temporal Differences in Taxon Richness and Abundance ....................... 53
3.4.3 Classification of Sites on the Milltown Lake Catchment ......................... 56
3.4.4 Relationship between the Macrophyte Community and Site Physico-
chemical Characteristics ........................................................................................... 69
3.5 Discussion ......................................................................................................... 72
3.5.1 The Macrophyte Flora of the Milltown Lake Catchment ......................... 72
3.5.2 Temporal Variation in Taxon Abundances and Richness ......................... 74
3.5.3 Classification of sites on the Milltown Lake Catchment. ......................... 75
3.5.4 Relationship between macrophyte community and site physico-chemical
characteristics ............................................................................................................ 78
3.6 Conclusions ....................................................................................................... 82
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3.6.1 To describe the macrophyte flora of a small drumlin catchment with
regard to relevant, comparable macrophyte typologies ............................................ 82
3.6.2 To assess the temporal variability in macrophyte taxon richness and
abundance in the catchment both within and between years .................................... 83
3.6.3 To provide a classification of sites on the Milltown lake catchment based
on the macrophyte community .................................................................................. 83
3.6.4 To highlight macrophyte community responses to physical and chemical
conditions in the Milltown Lake catchment .............................................................. 85
3.6.5 To assess the ability of the bank species to differentiate between sites and
to respond to physico-chemical conditions at those sites by comparison with the
aquatic species .......................................................................................................... 86
Chapter 4 Characteristics of Stream Bed Sediments in the Milltown Lake
Catchment 87 4.1 Introduction ....................................................................................................... 87
4.2 Aims .................................................................................................................. 89
4.3 Methods ............................................................................................................. 90
4.3.1 Sediment Sampling and Analysis Methodology ....................................... 90
4.3.2 Statistical Analysis .................................................................................... 91
4.4 Results ............................................................................................................... 92
4.4.1 Catchment Sediment Characteristics ........................................................ 92
4.4.2 Sediment Variability between Vegetated and Non-vegetated Patches ... 105
4.4.3 Sediment Variability between Seasons ................................................... 105
4.4.4 Relationships between Sites based on Sediment Physico-Chemical
Characteristics ......................................................................................................... 107
4.5 Discussion ....................................................................................................... 110
4.5.1 Sediment Variability between Vegetated and Non-vegetated Patches ... 112
4.5.2 Sediment Variability between Seasons ................................................... 113
4.5.3 Relationships between Sites based on Sediment Physico-Chemical
Characteristics ......................................................................................................... 114
4.6 Conclusions ..................................................................................................... 115
4.6.1 To describe the stream bed sediment characteristics of the Milltown Lake
catchment ................................................................................................................ 115
4.6.2 To compare physico-chemical characteristics of sediment collected from
vegetated and non-vegetated patches of the stream bed ......................................... 115
4.6.3 To compare physico-chemical characteristics of sediment collected in June
and samples collected in September ....................................................................... 116
4.6.4 To assess the variation in sediment characteristics across the Milltown
Lake catchment ....................................................................................................... 116
Chapter 5 Community Concordance Analyses between the Macrophyte Flora
and Macroinvertebrate Fauna of Milltown Lake Catchment .................................. 117 5.1 Introduction ..................................................................................................... 117
5.2 Aims ................................................................................................................ 119
5.3 Methods ........................................................................................................... 120
5.3.1 Biological Sampling ................................................................................ 120
5.3.2 Statistical Analysis .................................................................................. 121
5.4 Results ............................................................................................................. 122
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5.4.1 Within Community Concordance ........................................................... 122
5.4.2 Between Community Concordance ........................................................ 125
5.4.3 Temporal Variation in Community Concordance ................................... 130
5.5 Discussion ....................................................................................................... 133
5.5.1 Within community concordance ............................................................. 133
5.5.2 Between Community Variation .............................................................. 134
5.5.3 Temporal Variation in Community Concordance ................................... 136
5.6 Conclusions ..................................................................................................... 138
5.6.1 To assess within community concordance in the macrophyte community
by assessing concordance between bank macrophyte and aquatic taxa ................. 138
5.6.2 To assess concordance between the macroinvertebrate and macrophyte
communities, using datasets of bank macrophyte taxa and aquatic taxa ................ 138
5.6.3 To assess inter-seasonal and inter-annual variation in concordance
between the macrophyte and macroinvertebrate communities ............................... 139
Chapter 6 Performance of Selected Biological Water Quality Indicators in the
Milltown Lake Catchment ........................................................................................... 140 6.1 Introduction ..................................................................................................... 140
6.2 Aims ................................................................................................................ 143
6.3 Methods ........................................................................................................... 144
6.3.1 Macrophyte Sampling and Analysis Methodology ................................. 144
6.3.2 Macroinvertebrate Sampling and Analysis Methodology ...................... 147
6.3.3 Chemical Sampling and Analysis Methodology ..................................... 148
6.3.4 Statistical Analysis .................................................................................. 149
6.4 Results ............................................................................................................. 154
6.4.1 Water Quality in the Milltown Lake Catchment ..................................... 154
6.4.2 Inter-Seasonal and Inter-Annual Variability ........................................... 160
6.4.3 Inter-Comparison of Water Quality Indices ........................................... 162
6.4.4 Comparison between Water Quality Indices and Physico-chemical
Parameters of the Water Column ............................................................................ 165
6.5 Discussion ....................................................................................................... 171
6.5.1 Water Quality in the Milltown Lake Catchment ..................................... 171
6.5.2 Inter-Comparison of Water Quality Indices ........................................... 174
6.5.3 Comparison between Water Quality Indices and Physico-chemical
Parameters ............................................................................................................... 178
6.6 Conclusions ..................................................................................................... 180
6.6.1 To assess water quality in small first and second order streams in a
drumlin catchment using macroinvertebrate and macrophyte indices commonly in
use in Britain and Ireland ........................................................................................ 180
6.6.2 To compare the results of water quality assessments of the Milltown Lake
Catchment as determined by the macrophyte and macroinvertebrate indices ........ 180
6.6.3 To examine correlations between the various indices and the measured
physico-chemical parameters of the water .............................................................. 181
Chapter 7 Drivers of MTR Score in the Milltown Lake Catchment .................. 182 7.1 Introduction ..................................................................................................... 182
7.2 Variation in MTR Scores ................................................................................ 182
7.3 Conclusions ..................................................................................................... 187
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Chapter 8 General Discussion ................................................................................ 189 8.1 Context and Aims ........................................................................................... 189
8.2 Synopsis of the Results ................................................................................... 190
8.3 Implications for Management ......................................................................... 192
8.4 Limitations of the Current Research ............................................................... 194
8.5 Knowledge Gaps and Future Research ........................................................... 196
References……………………………………………………………………………...198
Appendices……………………………………………………………………………..223
Table of Abbreviations
AAS Atomic Absorption Spectrometry
ASPT Average Score Per Taxon
BOD Biochemical Oxygen Demand
BMWP British Monitoring Working Party
Ca Calcium
CBAS Canonical Based correspondence Analysis Score
CEC Commission of the European Communities
CFB Central Fisheries Board
Cu Copper
DCA Detrended Correspondence Analysis
DO Dissolved Oxygen
EC European Commission
EPA Environmental Protection Agency
EU European Union
Fe Iron
GIS Geographical Information System
GSI Geological Survey of Ireland
GWS Group Water Scheme
IFI Inland Fisheries Ireland
K Potassium
LOD Limit Of Detection
MDS MultiDimensional Scaling
Mg Magnesium
MIS Macrophyte Index Scheme
Mn Manganese
MTR Mean Trophic Rank
Na Sodium
NSSHARE North South Shared Aquatic REsources
NPWS National Parks and Wildlife Service
OM Organic Matter
OS Ordnance Survey
RDA ReDundancy Analysis
RICT River Invertebrate Classification Tool
RIVPACS River InVertebrate Prediction And Classification System
SRP Soluble Reactive Phosphorus
SSRS Small Streams Risk Score
TN Total Nitrogen
TRP Total Reactive Phosphorus
TP Total Phosphorus
UKTAG United Kingdom Technical Advisory Group
WFD Water Framework Directive
WRBD Western River Basin District
Zn Zinc
1
Chapter 1 Introduction
__________________________________________________________________
1.1 Background to the Study
1.1.1 Surface Water Quality in Ireland
Surface waters in Ireland are at risk from the effects of anthropogenic activities. Industry,
agriculture and domestic waste and wastewater contribute to the eutrophication, acidification
and physical degradation of rivers and lakes (Lucey 2009). Responsibility for monitoring water
quality in Ireland is held by the Environmental Protection Agency (EPA). Their monitoring
program includes both chemical and biological assessments of water and is carried out on a
long established network of sites throughout the country (EPA 2006). Overall water quality in
Ireland is good with the vast majority of our river and lake sites being given ‗good‘ status by
the EPA (83% of lakes and 70.2% of rivers) for the 2004 to 2006 reporting period (Clabby et al.
2008). Of great concern, however, is the loss of our ‗high‘ status river sites from 4.6% in the
1995/1997 reporting period (Lucey et al. 1999) to 2.7% in the 2001/2003 reporting period
(Toner et al. 2005). The number of sites reported at the lowest level of water quality was
increasing but was still low (0.6%). For the 2007 to 2009 reporting period, which included new
methods of assessment (including hydromorphological and conservation criteria), only 52% of
rivers and 47.3% of lakes were given ‗good‘ status. The most recent trend appears to be a
decrease in the number of ‗high‘ and ‗good‘ sites due to eutrophication from agricultural and
municipal sources and a decrease in the number of ‗bad‘ sites due to targeted programmes of
measures. As a result many sites are now of ‗moderate‘ status (Clabby et al. 2008; McGarrigle
et al. 2011).
The number of sites sampled by the EPA is limited by economic constraints and so there is no
possibility of their carrying out detailed sub-catchment studies. Although larger monitoring
networks are appropriate for long term trend analysis, they may miss the causal relationships
between local practices and poor water quality (Belle & Hughes 1983; Buck et al. 2004).
2
Studies of high spatial and temporal sampling intensity have been few in Ireland and most have
been driven by research needs in the area of conservation. Many are to be found in the grey
literature of the National Parks and Wildlife Service (NPWS), the Central Fisheries Board (now
Inland Fisheries Ireland (IFI)) and the EPA. These studies often lacked the integrated approach
of using both chemical and biological elements of water quality assessment and instead focused
on the elements the biota or the habitats which were of conservation value (Heuff 1984b;
Dromey et al. 1990). Other studies were of short duration and did not take longer term trends
into account (Moorkens et al. 1992). Giller et al. (1998) highlighted this lack of detailed
catchment-scale studies, and although examples exist of either biological surveys e.g. the work
done on macrophytes in the River Suir in the south of Ireland by Caffrey (1985), and of
chemical surveys e.g. the Blackwater TRACE project (Jordan et al. 2005; Arnscheidt et al.
2007; Jordan et al. 2007) and Lough Mask surveys (Hobbs et al. 2005; Donohue et al. 2005),
few intensive surveys have integrated both chemical and biological monitoring methods.
The limited number of sites sampled for the national monitoring program affects not only the
number but also the type of site selected. Smaller first and second order streams are neglected
in favour of larger downstream sites. The study sites are selected to represent the whole river
system, but small first and second order streams constitute as much as half of the country‘s
river network. As the Western River Basin District (WRBD) (2005) caution, if the risk of water
pollution is proportional to the length of the river network then these small streams may provide
a conduit whereby half the discharges to rivers occur. This may be of particular importance in
the drumlin belt which stretches across the island of Ireland from the north east of the country
to Clew Bay in the west. This area is comprised of many small hills with poorly drained alluvial
gleys, peaty gleys and inter-drumlin peats with extensive blanket bog at elevations above 150 m
(Zhou et al. 2000). This topography creates an intricate drainage pattern of small first and
second order streams and brooks feeding the many lakes that lie in the depressions between
hills. Drainage improvements during periods of agricultural intensification mean that flashy
storm flows and suppressed base flows now characterise the hydrology of the region (Douglas
et al. 2007). This complex hydrology due to multiple tributaries, non-uniform flow direction
and gradient has been shown to influence phosphorus (P) flux from septic tanks to water
(Arnscheidt et al. 2007). The high rural population intensity, intensive agriculture and
3
predominance of impermeable soil ensure that catchment typologies in this area conform to
neither the traditional agricultural model of diffuse P sources, nor the traditional urban model of
point P sources (Arnscheidt et al. 2007). The current study forms part of a larger project funded
by the Group Water Scheme (GWS) sector in the drumlin region to address the issues of source
protection including monitoring, risk assessment and remediation.
1.1.2 European Context
The commencement of the current study was set against the backdrop of the introduction of the
Water Framework Directive (WFD) (2000/60/EC) (CEC 2000). This piece of European
Commission (EC) legislation requires the improvement of surface water quality to ‗good‘ status
by member states throughout the European Union (EU). It is also required that this
improvement in water quality be measurable by a number of named biological elements
(macroinvertebrates, macrophytes, fish and phytobenthos). Some of these biological elements
are commonly used in Ireland to assess water quality and some had not been used prior to the
introduction of this piece of legislation. The tools to assess these elements of the biota were not
specified in the legislation. During the course of the present study new methods and
classification tools for each element were being developed and field trialled throughout Ireland
(e.g. Dodkins et al. 2005a; WRBD 2005; Free et al. 2006) and Europe (e.g. Schaumburg et al.
2004; Haury et al. 2006; Sniffer 2007).
The fundamental research aim of the current study was to assess the use of macrophytes as
biological indicators in a small drumlin catchment. This was achieved by firstly assessing the
relationship between the macrophyte community and environmental parameters, including site
physical characteristics, water and sediment chemistry. Secondly concordance in community
structure of macrophytes and macroinvertebrates, the more established bioindicator in Ireland
were analysed. Next, the water quality ratings derived using macrophyte and macroinvertebrate
water quality indices were compared. Finally the spatial distribution of species, index scores
and possible drivers were investigated to attempt to explain the variation in the macrophyte
community. These specific aims are further detailed in Section 1.7.
4
1.2 The Macrophyte Flora in Freshwater Biomonitoring
The definition of the term macrophyte is not immediately obvious. The word itself means large
plant and its use in freshwater biology has developed based on the interpretation of various
authors rather than on the basis of any taxonomic division. Aquatic plant assemblages constitute
a number of reinvasions of water by numerous terrestrial plant families and Preston & Croft
(1996) state that they are an ecological, rather than a taxonomic grouping. The term macrophyte
has been used to describe some or all of the following: plants associated with water (Clarke
2002), plants submerged in water (Schneider 2007), obligate hydrophytes (Haslam et al. 1975),
plants floating in water and plants rooted in water but emergent (Palmer et al. 1992). There is
also a broad division of how macrophytes are treated in the literature, based on whether they are
strictly aquatic species or emergent species associated with the riparian and littoral ecotones
(Holmes & Whitton 1977; Dodkins et al. 2005b; Oťaheľová et al. 2007). The term is used in
this study as defined in the Mean Trophic Rank (MTR) methods manual (Holmes et al. 1999)
and includes ‗large alga or higher aquatic plants (including bryophytes), observable to the
naked eye and nearly always identifiable when observed‘.
The most recent distribution atlas of the aquatic macrophytes in Ireland is provided by Preston
& Croft (1996). The authors highlight the need for published floras for the counties of Ireland
as the absence of such data renders analysis of trends in changes in distributions impossible.
The macrophyte flora of both the rivers and lakes of Ireland is also described in disparate
publications on individual water bodies (Krause & King 1994; King & Champ 2000; Pybus et
al. 2003) and on a larger scale in the grey literature (Heuff 1984a; Heuff 1984b; Matson 2006).
Checklists and keys for the British Isles provide much information on the flora of Ireland
(Haslam et al. 1975; Preston & Croft 1996) but Ireland has some species not currently found in
Britain (e.g. Hydrilla verticillata Presl., Pearsall 1936) and some species that are found in
Britain are not found in Ireland (e.g. Potamogeton compressus L., Haslam et al. 1975). While
classifications of both river (Holmes et al. 1998) and lake (Palmer et al. 1992; Duigan et al.
2007) macrophyte communities exist for Britain and Northern Ireland (Dodkins et al. 2005b;
McElarney & Rippey 2009), the macrophyte flora of Ireland‘s rivers and lakes still require
classification (Holmes 1999).
5
King & Caffrey (1998) suggest that the difference in the format in which data were collected by
different researchers (e.g. abundance scales, percentage cover, species lists) and the existence of
such data in the grey literature where it may not be readily accessed has hampered the use of
macrophytes as bioindicators in Ireland. Macrophytes are however, well suited as biological
indicators due to their stationary habit and the ease of identification provided by their size,
relative to other elements of the biota. This also makes them a relatively rapid and inexpensive
bioassessment tool (Caffrey 1985; Caffrey 1987; Schaumburg et al. 2004; Brabec &
Szoszkiewicz 2006). Macrophytes are further suited to their use as biological indicators of
water quality in the variation in their composition and relative abundances of species along a
gradient of nutrient enrichment. This response occurs both at the community level (Pearsall
1920; Butcher 1933; Holmes & Whitton 1977; Palmer et al. 1992; Szoszkiewicz et al. 2006; del
Pozo et al. 2010) and at the level of individual species responses (O‘Hare et al. 2010). A
number of indices have been developed throughout Europe to assess water quality using
macrophytes (e.g. Caffrey 1985; Holmes et al. 1999; Schaumburg et al. 2004; Free et al. 2006;
Haury et al. 2006). Macrophyte communities also show responses to physical disturbances at
sites, responding to changes in siltation levels (Holmes & Whitton 1977; Barko & Smart 1986),
dredging (Lubke et al. 1984), channelisation (Brookes 1986) and changes in flow regime
(Chambers et al. 1991; Franklin et al. 2008). Macrophyte communities have also been shown
to respond to the effects of acidification, whether the waters are naturally acid (Pearsall 1920;
Spence 1967; Riis et al. 2000) or if the acidification is anthropogenic (Monteith et al. 2005).
Macrophyte communities can also be structured by the physical characteristics of the sites at
which they occur. Aspects of the physical nature of the site which impact macrophyte
communities include flow, substrate, depth and light (Butcher 1933). Flow can impact both the
species of macrophyte that will be found at the site (Haslam et al. 1975; Preston & Croft 1996),
and the ability of those species to expand and increase in abundance once established
(Chambers et al. 1991; Sand-Jensen 1998; Cotton et al. 2006). In high flows species with fine,
dissected leaves e.g. Ranunculus spp., Oenanthe fluviatilis (Bab) Coleman may be most
successful, as this type of morphology gives little resistance to the water. Other successful
adaptations to high flows include members of the Bryophyta e.g. Hygroambylestegium fluviatle
(Hedw.) Loeske and Scapania undulata L. Dum, whose flattened growth habit also provide
6
little resistance (Watson 1981). Macrophytes in strong flows may be broken or uprooted if the
force of the flow is greater than the ‗anchoring strength‘ of the plant (Haslam 1978).
Substrate type affects macrophytes in a number of ways. Firstly substrate size can impact the
ability of some macrophyte species to root and establish on the river bed, with smaller substrate
sizes encouraging higher plant growth (Haslam et al. 1975; Dodkins et al. 2005a). Larger
substrates are often associated with the Bryophyta (Scarlett & O‘Hare 2006). Substrate can also
affect macrophyte nutrition as macrophytes can obtain their nutrients from both the sediment
and the water column (Barko et al. 1991) and macrophyte distribution has been related to
substrate physico-chemical properties (Johnson & Ostrofsky 2004, see also Section 1.4 below).
The influence of depth on macrophyte community composition has been related to increasing
light attenuation with increasing depth (Butcher 1933). Previous studies have highlighted
species which show associations with increasing depth e.g. Octodiceras fontanum (Bach. Pyl.)
Lindb (Johnson & Ostrofsky 2004; Scarlett & O‘Hare 2006) and those which show associations
with shallow sites e.g. Rorippa nasturtium aquaticum agg. (Holmes 2009). The degree of
shading can also impact the macrophyte community (Butcher 1933; Staniszewski et al. 2006)
and can result in lower species diversity at sites with higher degrees of shading (Matson 2006).
1.3 Physico-chemical Parameters of Water Quality
The WFD places much of its emphasis on the biological elements as indicators of water quality,
but physico-chemical characteristics must also be measured as these act as supporting elements
in the assessment of overall status. The information obtained by use of the physico-chemical
parameters of water quality only provides a snapshot of water quality at the time of sampling.
However, the information provided can indicate the nature and severity of pollution impacts.
The following parameters are of most relevance in monitoring water quality for the effects of
agriculture and the health of fisheries and aquatic biota (Meybeck et al. 1996).
The pH of water measures how acid or alkaline that water is. pH is important as it influences
many biological (e.g. osmoregulation and hatching) and chemical processes (e.g. ammonia
toxicity, chlorine disinfection efficiency and metal solubility). pH in natural waters generally
7
ranges from around pH 4.5 for acid, peaty waters to pH 10 for waters with intense
photosynthetic algal activity (EPA 2001). The optimum range for salmonid fisheries is pH 6.5
to 8.5 (Minister for the Environment, Heritage and Local Government 2009). Although pH is a
conservative parameter in water of good quality, diel variations may occur in waters impacted
by eutrophication in which algal photosynthesis dominates (McGarrigle 2001).
The temperature of a river is generally influenced by climate and season, (although thermal
discharges will also have an impact). The range in Irish waters is from freezing point to about
25ºC (EPA 2001). Temperature exerts an important influence on the equilibria between certain
constituents of water (ionisation of ammonia (NH3) to ammonium (NH4)) and on the
concentrations of others (dissolved oxygen). Furthermore, due to the impacts of steep
temperature gradients on fish, the Freshwater Fish Directive (78/659/EEC) limits temperature
changes downstream of thermal discharges.
The nutrients P and nitrogen (N) are of greatest interest in the assessment of water quality due
to their role in the process of eutrophication. P is the nutrient most likely to be limiting in
freshwater systems and is a driver of primary production in these systems due to its importance
to algal communities (Jeppesen 1998). P is most frequently described either in terms of total P
in lakes, or orthoP in rivers. OrthoP is often used as this is the fraction which is most readily
available for biological uptake (EPA 2001). The total, unfiltered fraction (Total Reactive
Phosphorus) is measured by the EPA as this represents the most bioavailable fraction (Martin
McGarrigle, EPA, pers. comm.). TRP includes dissolved P, colloidally bound P and some
particulate P (termed interstitial P) (Reynolds & Davies 2001). However the filtered fraction
(filtered through 0.45 µm pore size) (Soluble Reactive Phosphorus) is also widely measured to
eliminate potential interference from particulate matter in the spectrophotometric stage of
analysis (EPA 2001). The Surface Water Regulations (S.I. 272/2009) introduce P standards for
assessment of Irish rivers. High status sites should have an annual median concentration of less
than 0.025 mg/L TRP and good status sites should be less than 0.035 mg/L. Sources of P in
freshwaters include run-off and leaching of inorganic fertilisers from agricultural land and
discharge of P-containing detergents into municipal waste water (Meybeck et al. 1996).
8
Nitrogen is also of importance to freshwater quality as it is necessary for primary production by
algae. However, it is often naturally present in sufficient concentrations so as not to limit
growth. In systems where P inputs are high, N reserves may become depleted through plant
uptake (Jeppesen 1998). The system may then change to a situation of N limitation. Nitrogen
forms a complex cycle in fresh waters with microbial activity governing much of the
relationship between nitrogen, ammonium, nitrate and nitrite in the water column. Nitrogen in
the water column tends to be represented as either ammonium or nitrate. Ammonium exists in
equilibrium with ammonia, but within the range of pH associated with natural waters the
majority will be as ammonium (Chapman & Kimstach 1996). The Surface Water Regulations
(S.I. 272/2009) introduce total ammonia standards for assessment of Irish rivers. High status
sites should have a mean annual ammonia concentration of less than 0.04 mg/L N and good
status sites should have a mean annual ammonia concentration of less than 0.065 mg/L N.
Nitrite is also commonly analysed for but will usually constitute only 1 - 2 % of the nitrate level
(EPA 2001). Sources of nitrogen include urban wastewaters, industrial wastes and agricultural
practices such as use of fertiliser and discharge of waste from livestock (Meybeck et al. 1996).
Ammonia is associated with organic inputs and nitrate with chemical fertiliser use. The effects
of increasing nutrient concentrations include increased primary production, marked diel
changes in dissolved oxygen concentrations and pH leading to fish kills and impairment of
water quality for the majority of uses.
Dissolved oxygen (DO) provides a measure of the oxygen available for respiration in a water
body. DO provides an excellent indication of water body health, especially when measured
consistently over the year (and at certain times throughout a 24 hour period). Regular
measurement may be required as DO concentrations can show the impact of algal
photosynthesis and respiration (McGarrigle 2001). DO concentrations can be high during the
day suggesting good water quality, but this may be due to algal photosynthesis and there may
be a corresponding depletion of DO concentrations at night when the system is dominated by
algal respiration. If DO concentrations drop below ~6 mg/L there may be implications for fish
health and may result in fish kills (McGarrigle 2001). Another measure of water quality that
utilises DO is the five day Biological Oxygen Demand test (BOD5), which measures the
9
amount of DO used during microbial degradation of organic matter in a water sample over five
days. Samples with a high BOD suggest excessive organic inputs to the system.
Conductivity measures the ability of the water to carry and electrical current and is a measure of
all the ions in solution. As conductivity can be affected by dissolved solids and all the major
ions it can be used to indicate inputs to the system, which may be detrimental to water quality.
It can be of particular use as a rapid indicator due to ease of measurement in the field. Changes
in conductivity may also reflect changes in alkalinity or hardness (Meybeck et al. 1996). Total
Dissolved Solids (TDS) is related to conductivity and measures ionised and non-ionised matter.
TDS can be calculated from conductivity and in unpolluted waters is usually between 55 and 75
% of that value depending on the concentrations of sodium, chloride and sulphate (Chapman &
Kimstach 1996). Waters with low dissolved solids have low buffering capacity (low resistance
to pH change)
Alkalinity is the sum of the titratable bases. Those which tend to dominate alkalinity in clean
waters are the carbonate and bicarbonate ions. As the concentrations of these ions are mostly
related to geology, in clean waters alkalinity tends to be a conservative parameter. However,
alkalinity will also be affected by concentrations of the basic phosphate ion. Waters with low
alkalinity (<24 mg/l) have low buffering capacity and may be vulnerable to pH changes
(Meybeck et al. 1996).
1.4 Interactions between Macrophytes and Stream Bed Sediments
It has been shown that many species of macrophyte are able to derive their nutrients from the
sediments in which they root (Carignan & Kalff 1980; Barko & Smart 1986). It is, however a
matter of debate as to which is the principal source of nutrients - the water column or the
sediment (Barko et al. 1991). Carignan & Kalff (1980) suggest that in mesotrophic lake
conditions the proportion of P provided by the sediment can be close to 100% and Chambers et
al. (1989) found that macrophytes growing in rivers obtained in excess of 70% of required P
from the sediment. This utilisation of sediment nutrient highlights the importance of
10
macrophytes in the cycling of nutrients between the sediment layers and the water column
(Wigand et al. 2001).
Macrophytes can interact with sediment by changing the availability of sediment nutrients. P
availability can be decreased by the release of oxygen into the sediment through plant roots.
This addition of oxygen can affect the redox potential of the sediment, binding P to iron
compounds (Jaynes & Carpenter 1986; Pedersen et al. 1995). This impact on redox potential
does not hold where inputs of organic nutrient are high and in such water bodies it has been
found that total P (TP) release can be higher in vegetated sediment than in non-vegetated
sediment (Stephen et al. 1997). The effect of this interaction between macrophyte roots and
sediment may not be as pronounced in rivers as in lakes as more habitually riverine species do
not produce as much oxygen through their roots (Sand-Jensen et al. 1982).
N is more soluble and more mobile than P and as a result the relationship between N and
sediment is poorly understood, however, it has been shown to be determined by microbial
processes (Heathwaite et al. 1996). Where water column N concentrations are low,
macrophytes have been shown to take their N from sediment sources (Li et al. 2010). Indeed, in
laboratory experiments Myriophyllum spicatum L. has been shown to derive all of its N needs
from sediment (Best & Mantai 1978). Although most studies have been concerned with the
more stable lake sediments there has been some work on the relationship between stream bed
sediments and macrophytes (Chambers et al. 1991; Clarke & Wharton 2001; Schneider &
Melzer 2004).
Most studies of nutrient requirements of macrophytes have focused on P and N as these are the
nutrients most likely to become limited under varying circumstances in freshwater systems.
Among the other elements required for healthy growth of macrophytes, potassium (K) and
calcium (Ca) may be the most important (Barko et al. 1991). In a study on Lake Pleasant,
Pennsylvania (Johnson & Ostrofsky 2004) sediment K concentrations did not correlate with
other measured sediment nutrient concentrations, nor did it define classifications between sites
in that study, suggesting that the relationships were not linear. When unimodal techniques were
applied, K was shown to be significant in explaining macrophyte community variation. Hobbs
11
et al. (2005) found that Ca was significantly, negatively correlated with sediment of a high
percentage of organic matter (%OM), but did not relate sediment characteristics with
macrophyte community responses.
While the sediment layer and the water column are connected through the processes of
macrophytes, the relationship between sediment chemistry and water chemistry in a given
system is complex and has not been adequately described (Clarke & Wharton 2001). Dissolved
P has been shown to readily exchange between the surficial sediments and the water column,
where the sediment is saturated with P (Reynolds & Davies 2001). However, no clear
relationships have been defined between sediment nutrient concentrations and water column
nutrient concentrations (Stutter et al. 2007) and factors other than water and sediment nutrient
concentrations may govern the equilibrium between the two (Clarke & Wharton 2001).
While macrophytes will affect the chemistry of the sediment in which they root through
interactions such as those mentioned above, they may also impact the amount of sediment
present in a stream. Certain macrophytes species can increase stream sediment stability by the
binding action of their roots (Sand-Jensen 1998) and large stands of macrophytes can trap
sediment and allow it to accumulate at a site (Rooney et al. 2003; Cotton et al. 2006). Where
sediments are rich in nutrient, such accumulations may constitute an important source of N and
P and may be significant in nutrient-poor streams (Haslam et al. 1975). Stands of macrophytes
in rivers will also impact flow velocity at a local scale providing flow heterogeneity across the
stream bed and optimising conditions for photosynthesis within the macrophyte stand (Sand-
Jensen & Mebus 1996). As well as macrophytes altering sediment characteristics, sediment
nutrient concentrations can also impact the growth and morphology of macrophytes. Plants with
a high root:shoot ratio are characteristic of nutrient poor sediment as plants increase root
surface area to maximise nutrient uptake, the converse is true in nutrient rich sediments (Barko
& Smart 1986).
Studies of macrophyte communities have long recognised the influence of substrate type on
macrophyte distribution (Pearsall 1936; Butcher 1927; Haslam et al. 1975). More recent studies
have quantified these effects in conjunction with other physical and chemical characteristics of
12
macrophyte habitats. Barendregt & Bio (2003) highlighted stream bed sediment as explaining
variability in macrophyte communities at both the local and site scale of assessment. Triest
(2006) found that there was a negative correlation between water quality indices based on
macrophytes and fine substrate particle size and a positive correlation with large substrate size.
In the same study multivariate analyses showed the importance of sediment total N (TN) in
explaining the variability in the macrophyte community sampled. Kuhar et al. (2007) noted that
substrate characteristics such as sediment accumulation and dominant substrate size were
significant in explaining variation in the macrophyte communities surveyed and that there was a
positive relationship between smaller substrate size and macrophyte development and growth.
Published studies of stream bed sediments in Ireland are, for the most part, studies of sediment
metal concentrations for the purpose of monitoring the effects of mining and motorway run-off
(Ni Mhairtin et al. 1999; Yau & Gray 2005; Bruen et al. 2006). Such heavy metal
contamination is not characteristic of Irish rivers and relationships between sediment
characteristics and the biological communities present were not explored. Analysis of stream
bed sediments in an upland catchment of moderate agricultural activity in Scotland found that
there were significant relationships between stream bed sediments and the macroinvertebrate
and algal communities (Stutter et al. 2007). Correlation analyses were carried out between
sediment chemical parameters and macroinvertebrate water quality indices and between
sediment chemical parameters and chlorophyll a concentrations, but no analyses were carried
out on the macrophyte community.
1.5 The Macroinvertebrate Fauna in Freshwater Biomonitoring
The freshwater macroinvertebrate fauna of Ireland is well described with checklists available
for most groups (e.g. Woods 1974; McCarthy 1975; Costello 1988; Ashe et al. 1998; Trodd et
al. 2005). Much of the fauna of Ireland can be found in Britain but the Irish fauna is missing
many of the species present there (Wright et al. 2000). There are also some species unique to
Ireland that are not found in Britain and whose presence is attributed to our glacial and post-
glacial history (Giller et al. 1998). Of all the groups present in freshwater habitats the following
are of most interest in current water quality monitoring; Ephemeroptera, Odonata, Plecoptera,
13
Hemiptera, Coleoptera, Megaloptera, Trichoptera, Diptera, Hirudinea, Planaria, Mollusca and
Malacostraca.
Macroinvertebrates are perhaps the element of the biota most widely used as biological
indicators in Europe and North America. The advantages of using benthic macroinvertebrates
as biological indicators are described in several good reviews of the practice (De Pauw &
Persoone 1979; Metcalfe 1989; Rosenberg & Resh 1993) which are summarised here.
Macroinvertebrates are ubiquitous in the target habitat and they are easy to collect and identify.
They respond to a number of different pollutants or stressors and that response is evident along
a gradient of that pollutant or stressor. Furthermore, these responses are well documented in the
literature. Macroinvertebrate communities can be used to provide information on water quality
that has been assimilated over a longer period of time than that which can be gathered by on-off
chemical sampling means. This is due to the relatively long life cycles of many taxa.
Macroinvertebrate communities can also be cheaper to sample as they require less expensive,
specialist equipment than chemical sampling and analysis alone. The identification of
macroinvertebrates is also simplified by the many keys available and as such their taxonomy is
well known.
Macroinvertebrate communities have been shown to respond to physical disturbance
(Clenaghan et al. 1998; Murphy & Davy-Bowker 2005), organic pollution (Hawkes & Davies
1971; Whitehurst & Lindsey 1990; Clabby et al. 2008) and acidification (Weatherley &
Ormerod 1987; Monteith et al. 2005; Cruikshanks et al. 2008; Gray & Delaney 2008). Physical
disturbance such as that which occurs during channelisation, drought or flood events can result
in dramatic losses both of species and of numbers of individuals. In the event of a flood it has
been shown that colonisation of species from upstream refugia will return the community to its
former diversity with time (Watanabe et al. 2005) and drought can bring about a change from a
lotic type community to a lentic one (Bond et al. 2008). Channelisation events can cause more
long term damage due the destruction of the habitat at the site (Tavzes et al. 2006; Horsák et al.
2009).
14
Due to the status of macroinvertebrates as indicators of freshwater quality, there has been a
large amount of research into their responses to organic pollution. Reviews of the subject
(Hawkes & Davies 1971; De Pauw & Persoone 1979; Metcalfe 1989) and various water quality
indices (WRBD 2005; Sniffer 2007; Clabby et al. 2008) summarise the response of
macroinvertebrate communities as follows; lotic freshwater sites of high water quality will be
well represented by the Plecoptera and Ephemeroptera, members of the Trichoptera,
Coleoptera, and Mollusca will also be present in reasonable numbers. Members of the Diptera,
Crustacea and Oligochaeta will be few. Overall diversity may not be that high at such sites due
to the low nutrient availability. With increasing levels of enrichment the community will shift
toward one dominated by the Diptera (especially the Chironomidae and Simuliidae),
Oligochaeta and Malacostraca (especially the Asellidae and the Gammaridae).
The response of the macroinvertebrate fauna to acidification in Ireland has been examined in
relation to the effects of forestry in acid sensitive areas (Tierney et al. 1998; Cruikshanks et al.
2008) as have other elements of the biota (Bowman & Bracken 1993). Although acidification
has been shown to affect only a small minority of sites in the country (Bowman et al. 1996), the
response of members of the Ephemeroptera in particular, to acidification have been noted and
their absence from upland sites in Co. Wicklow in the above studies suggested they were at the
lower limit of their acid tolerance (Tierney et al. 1998).
1.6 Community Concordance Analyses
Communities are concordant when they respond similarly, but independently to the same
pressures (Paszkowski & Tonn 2000). While the WFD requires the use of macroinvertebrates,
macrophytes, fish and phytobenthos in the assessment of water quality by the EPA, smaller
scale assessment programs such as that envisaged for the GWS catchments will find the cost of
monitoring all of these biological elements prohibitive. One community might be able to act as
a surrogate for another if it can be shown that the communities in question are concordant
(Paavola et al. 2003). Application and reliance on one community will require less time and
expertise for analysis and interpretation and therefore less money. However, where assessments
on a number of communities must be integrated, knowledge of community concordance may
15
aid understanding where different communities assign different status to a water body (Moss et
al. 2003) and where communities are concordant, add confidence to the assessment (Hatton-
Ellis 2008).
Abiotic factors affecting community concordance include spatial variation (Heino 2002;
Paavola et al. 2006; Dolph et al. 2011), temporal variation (Mykrä et al. 2008b), environmental
conditions (Heino 2002; Mykrä et al. 2008a; Heino et al. 2009b; Gioria et al. 2011) and the
methodology used (Matson 2006; Gioria et al. 2011). Many studies have been carried out at the
regional scale and it is at this scale that concordance between taxonomic groups has been
shown to be strongest (Heino et al. 2009b). This was evident as a correlation between variation
in the communities surveyed and latitude and longitude and was attributed to the effects of
post-glacial colonisation in Fennoscandia (Paavola et al. 2006). Studies of concordance at
smaller, local scales have typically reported lower levels of community concordance (Heino et
al. 2005) and so, studies of community concordance should be carried out on the target
communities at the scale at which they might be substituted to ensure significant concordance
and increase confidence in the results obtained (sensu Paavola et al. 2006). In Ireland there has
been no analysis of concordance at a smaller spatial scale than that attempted by Matson
(2006), who compared macrophytes and macroinvertebrates from sites across the country, and
so the effects of local scale responses to environmental pressures and their effects on
concordance have not been elucidated.
The effect of inter-annual variation on concordance was studied by Mykrä et al. (2008b) who
found that different groupings of macroinvertebrates were concordant in Finnish boreal streams.
Although variation in biological communities can be attributed to seasonal effects (Brabec &
Szoszkiewicz 2006; Cotton et al. 2006; Sporka et al. 2006; Callanan et al. 2008), studies of the
effect of inter-seasonal variation on concordance are not evident in the literature. Temporal
variation in community concordance might be expected where the two communities respond to
pressures along different time scales and this may cause a lag in concordant responses within
the two communities (Mykrä et al. 2008b).
16
In assessing community concordance a number of methods have been used by previous authors,
which have included simple bivariate statistics comparing one measure of the community e.g.
richness, with another (Smith et al. 2007). However, modern concordance analyses increasingly
recognise the ability of multivariate methods to incorporate more information on communities
and their responses (Heino et al. 2009b). The development of suitable software programs has
the potential to greatly advanced research into this area e.g. Primer v6. Multivariate analyses of
community concordance include imposing classifications obtained through clustering analysis
of one community onto the other. The ‗goodness of fit‘ of that classification is then assessed
(Paavola et al. 2003; Dodkins et al. 2005b). Another approach is the use of Mantel type tests
which are element by element correlations of two similarity matrices (Heino et al. 2009a; Lopes
et al. 2011).
1.7 Water Quality Indices based on Macrophytes and Macroinvertebrates
A number of water quality indices, based on the use of the macrophytes and macroinvertebrates
were developed in Ireland prior to the introduction of the WFD, while others have recently been
introduced in light of its requirements. The earliest index of water quality based on
macrophytes for Irish rivers was the Macrophyte Index Scheme (MIS) (Caffrey 1985). The MIS
is based on expert opinion on the responses of particular macrophyte species to organic
pollution and is presented as a five point scale. The MIS was designed to provide results that
would be directly comparable with the EPA‘s Q-value system for macroinvertebrates (Clabby
et al. 2008) and so the scales used in both indices are the same (Q1-Q5). The results obtained in
the study of Caffrey (1985) were not compared to any chemical or physical aspects of the sites
studied, but were compared with the Q-value for macroinvertebrates assigned during parallel
surveys. Although the study showed there was agreement between the two indices there has
been no advance in the use of the index in Ireland. This may be due to the short species list
which may limit its use in many catchments.
More recently the MTR index developed in Britain (Holmes et al. 1999) has been used in
Ireland (Matson 2006). This index was designed to help meet the requirements of the Urban
Wastewater Treatment Directive (91/271/EEC) (CEC 1991). The MTR is based on expert
17
opinion of plant responses to pollution pressures (Holmes et al. 1999) and has been shown to
correlate significantly with P concentrations (Dawson et al. 1999). It was developed for use as
an indicator of water quality up and downstream of a given wastewater discharge point. As the
index does not take river typologies into account it is recommended that scores should only be
compared at up and downstream sites or at the same site during different years. The use of the
MTR in an intensive catchment study, such as in the present study, is suggested by the MTR
authors (a eutrophication audit) though no work has been published in Ireland which uses the
index in this way. The MTR has been used for the purposes of intercalibration of European
macrophyte indices for the WFD and has been shown to perform well with some modifications
(Birk et al. 2006). The MTR is currently being trialled by the EPA in light of the requirements
of the WFD (Martin McGarrigle, EPA, pers. comm.) but has not been adopted in Britain where
a method called LEAFPACS has since been introduced (WFD UKTAG 2008a).
A new macrophyte index has also been developed for use on the island of Ireland in assessing
ecological status in accordance with the requirements of the WFD. CBAS (Canonical Based
correspondence Analysis Score) is a statistically derived index which is based on plant
responses to changes in ecological quality along a gradient of six metrics (Dodkins et al.
2005a). The score assigned to a site takes into account the degree of departure of that site from
reference conditions and allows sites to be compared from all river types. While the score was
developed from a dataset of sites on the island of Ireland, few published studies have presented
CBAS data and the method requires further testing and validation (NSSHARE 2008). This will
include an assessment of the bioindicator potential of bank macrophytes, as such CBAS
currently recommends that bank species be recorded during surveys. CBAS is also currently
being trialled by the EPA in light of the requirements of the WFD (Martin McGarrigle, EPA,
pers. comm.).
The application of macroinvertebrates as biological indicators of riverine water quality has long
been in use in Ireland and has formed the backbone of the EPA‘s water quality monitoring since
the 1970s. In fact, the macroinvertebrate sampling program in Ireland provides a unique
resource as the same sites have been sampled using the same methodology and in some cases
by the same people for decades. The EPA Q-value system (Clabby et al. 2008) is an expert
18
derived index and provides information on water quality on a five point scale (Q1-Q5).
Intermediate scores represent transitional stages. The Q-value system also takes into account
the dissolved oxygen (DO) concentrations at the site and the presence of algae and sewage
fungus.
Another assessment technique using macroinvertebrates was developed in Ireland more recently
as a risk assessment tool for use in the implementation of the WFD. The Small Streams Risk
Score (SSRS) (WRBD 2005) was developed as a rapid assessment tool for bankside use. It was
developed for use in streams of less than two metres in width as this size of watercourse is not
monitored by the EPA program. It was developed on a small number of sites in the west of
Ireland but its application is seen as being suitable for nationwide use. The SSRS classes sites
into one of three risk categories, ‗at risk‘, ‗probably at risk‘ and ‗probably not at risk‘. The
SSRS does not require a high level of taxonomic expertise as it has been designed so that only
taxa which can be identified in the field are used in its calculation.
The British Monitoring Working Party (BMWP) (Armitage et al. 1983) score was developed in
Britain for use in monitoring organic enrichment of rivers. This index is in the form of a sum of
scores given to each of a number of taxa derived from their tolerance of pollution. The scores
given to these taxa were based on expert opinion but have since been tested against
environmental datasets (Wright et al. 1984; Wright et al. 2000). However this method of
expressing the impact of pollution on the fauna has been criticised as being too reliant on the
number of taxa present and so the results of the BMWP are often represented as an Average
Score Per Taxon (ASPT) by dividing the BMWP by the number of scoring taxa present. This
methodology has been used in studies of headwater sites (Callanan et al. 2008) and high status
sites (Walsh 2005) in Ireland and has been shown to provide an appropriate measure of water
quality. The BMWP is also used in modified forms in countries across Europe (Zamora-Muñoz
et al. 1995; Iliopoulou-Georgudaki et al. 2003; Czerniawska-Kusza 2005). The ASPT has since
been developed into a WFD compliant tool for use in Britain, incorporating river typologies
(WFD UKTAG 2008b).
19
While each of the indices above has been used in Ireland and results obtained from their use can
be compared with that of other studies, not all recommend themselves for further use and most
require further testing. The MIS has too limited a species list (Caffrey 1987) to be used in the
Milltown Lake catchment. The MTR has only been used in one study in Ireland (Matson 2006)
and its relationship with water quality here has not been tested. CBAS, although developed on
Irish datasets (Dodkins et al. 2005a), has not been widely used. The Q-value system was
developed in Ireland, but on larger rivers than that of the Milltown Lake Catchment (Clabby et
al. 2008). The SSRS was developed in Ireland for small streams, but has been little used or
tested and both the BMWP and the ASPT were developed in England, without reference to the
Irish fauna.
The use of water quality indices at the high spatial intensity employed in this study is not often
possible due to economic constraints imposed on monitoring programmes (Belle & Hughes
1983). Some indicator communities have been shown to integrate characteristics at varying
spatial scales e.g. regional, catchment and site (Barendregt & Bio 2003; Buck et al. 2004;
Paavola et al. 2006; Rios & Bailey 2006; Ciesielka & Bailey 2007; Feld & Hering 2007; Zilli &
Marchese 2011). These characteristics include geology, alkalinity, flow and substrate. The
impact of such characteristics on water quality indices is acknowledged (Holmes et al. 1999),
but it is not certain if their use at high spatial intensity will be overpowered by these
characteristics (Thiébaut et al. 2006; Demars & Edwards 2009). This may result in a damping
down of pollution impacts. However, the homogenous nature of some of these characteristics
e.g. geology in the catchment may reduce the background noise and provide a clearer picture of
the pollution impacts (Holmes et al. 1999).
As part of the WFD, exercises on intercalibration of metrics for each of the biological elements
are being carried out. Intercalibration attempts to find a common scale along which to compare
member states‘ metrics (Furse et al. 2006) and to assign comparable ecological quality ratings
to the boundaries of the water quality classes used (Buffagni et al. 2007). Factors which have
complicated the intercalibration process have included difficulties in determining reference
conditions (Nijboer et al. 2004; Kelly-Quinn et al. 2009), lack of suitable data (Martin
McGarrigle, EPA, pers. comm.) and poor relationships between member states‘ metrics and the
20
common metric (Kelly et al. 2009). As a result of these difficulties not all biological elements
have intercalibrated tools and where the tools have been intercalibrated it has not been carried
out at all quality class boundaries (McGarrigle & Lucey 2009; Bennett et al. 2011). To further
the intercalibration process the required relationships between the member states‘ metrics and
the common metric have been lowered to r2=0.5 in some instances (Birk & Willby 2010) and
even r2=0.3 (Ruth Little, EPA, pers.comm.).
1.8 Aims of the Current Research
The fundamental research aim of this study was to assess the use of macrophytes as biological
indicators in a small drumlin catchment. There were five main areas of investigation:
1.8.1 The Macrophyte Flora of the Milltown Lake Catchment (Chapter 3)
To describe the macrophyte flora of a small drumlin catchment with regard to relevant,
comparable macrophyte typologies
To assess the temporal variability in macrophyte taxon richness and abundance in the
catchment both within and between years
To provide a classification of sites on the Milltown lake catchment based on the
macrophyte community
To highlight macrophyte community responses to physical and chemical conditions in
the Milltown Lake catchment
To assess the ability of the bank species to differentiate between sites and to respond to
physico-chemical conditions at those sites by comparison with the aquatic species
1.8.2 Characteristics of Stream Bed Sediments in the Milltown Lake Catchment
(Chapter 4)
To describe the stream bed sediment characteristics of the Milltown Lake
catchment
21
To compare physico-chemical characteristics of sediment collected from vegetated
and non-vegetated patches of the stream bed
To compare physico-chemical characteristics of sediment collected in June and
samples collected in September
To assess the variation in sediment characteristics across the Milltown Lake
catchment
1.8.3 Community Concordance Analyses between the Macrophyte Flora and
Macroinvertebrate Fauna of the Milltown Lake Catchment (Chapter 5)
To assess within community concordance in the macrophyte community by
assessing concordance between bank macrophyte and aquatic taxa
To assess concordance between the macroinvertebrate and macrophyte
communities, using datasets of bank macrophyte taxa and aquatic taxa
To assess inter-seasonal and inter-annual variation in concordance between the
macrophyte and macroinvertebrate communities
1.8.4 Assessment of the Biological Water Quality Indicators used in the Milltown
Lake Catchment (Chapter 6)
To assess water quality in small first and second order streams in a drumlin
catchment using macroinvertebrate and macrophyte indices commonly in use in
Britain and Ireland
To assess inter-seasonal and inter-annual variability in the water quality indices
used
To compare the results of water quality assessments of the Milltown Lake
Catchment as determined by the macrophyte and macroinvertebrate indices
To examine correlations between the various indices and the measured physico-
chemical parameters of the water
22
1.8.5 Assessment of Spatial Drivers of Water Quality in the Milltown Lake
catchment (Chapter 7)
Chapter 7 uses analyses carried out in earlier chapters including spatial distribution of
species, index scores and possible drivers to attempt to explain the variation in the
macrophyte community in Milltown Lake catchment.
Finally, Chapter 8 examines the findings of each of the main areas of investigation above and
how they relate to each other, how they add to the current knowledge and what areas they
highlight for future study.
23
Chapter 2 Site Description of the Milltown Lake Catchment
__________________________________________________________________
2.1 Milltown Lake Catchment
The Milltown Lake catchment is situated north of Castleblaney, Co. Monaghan in the northeast
of Ireland (H 842 226). This catchment is a sub-catchment of the Fane River which forms part
of the Neagh-Bann International River Basin District. The catchment is 30.6 km2, 8 km
2 of
which is located in Co. Armagh (Figure 2.1). The catchment is located within the drumlin belt
which extends from the north east of the country to Clew Bay in the west. This topography is
famed for its undulating landscape, with many hills dotted throughout and the depressions in
between filled with lakes or bogs. This area is comprised of many small hills with poorly
drained alluvial gleys, peaty gleys and inter-drumlin peats with extensive blanket bog at
elevations above 150 m (Zhou et al. 2000). This creates an intricate drainage pattern of small
first and second order streams and brooks feeding the many lakes that lie in the depressions
between hills. Drainage improvements during periods of agricultural intensification mean that
flashy storm flows and suppressed base flows now characterise the hydrology of the region
(Douglas et al. 2007). Characteristics of this topography which have implications for water
quality include the steep slopes of the hills and poorly draining soils. The catchment overlies
the Silurian and Ordovician shales, greywackes and grits which make up the central uplands in
the geological division of the county. These are classed as non-aquifers and all the aquifers in
the catchment are classed as ‗low vulnerability‘ (www.gsi.ie). There are two soil types
encountered in the catchment. The most widespread soils are dry podzols but these are shallow
and overlie a compact glacial till which impacts drainage. The second soil type which occurs in
the catchment is found on Mullyash Mountain (319 m) to the eastern side of the catchment; this
soil type consists of shallow, blanket peat which overlies the glacial till (www.gsi.ie).
24
Figure 2.1 Map of the Milltown Lake catchment
25
The catchment is drained by the Drumleek River, which is fed by a number of smaller
tributaries (Figure 2.1). These streams are not named either on the Ordnance Survey (OS) maps
or locally and so for the purposes of this study, have been named for the area from which they
rise. Two of the larger tributaries of the Drumleek are fed by small lakes. The TV tributary is
fed by Carnagh Lake (H 82882 29062) in Tievenamara. This forest is deciduous but
downstream there is a coniferous plantation. The GO tributary is fed by Gentle Owen‘s lake (H
83931 30067). The MA stream drains Mullyash Mountain. The summit of this hill is afforested
but the slopes are under agriculture. The CN tributary drains the smaller Connory‘s Hill and the
DG tributary rises in Drumgallon. Milltown Lake itself is located north of the village of Oram
(H 84237 22551) and has an area of 22 ha, a maximum depth of 12 m and a mean depth of 5.5
m (Table 2.1). Figure 2.2 shows the profile of the main tributaries which feed into Milltown
Lake.
75
95
115
135
155
175
195
215
235
220 230 240 250 260 270 280 290 300Northings (1 devision is 1km)
Hei
ght
(m)
Figure 7.1 Profiles of the main tributaries of the Milltown Lake catchment. Black = MA, Red =
TH, Blue = TV, Green = GO. Points on the plot do not relate to site locations in the current
study. Source: Carson, 2011
26
Table 2.1 Lake and catchment characteristics for the Milltown Lake catchment (Carson 2011)
Lake Catchment Characteristics Milltown Lake Catchment
Lake area 22 ha
Mean lake depth 5.5 m
Max lake depth 12 m
Catchment area 30.6 km2
Mean annual rainfall 800-1000 mm
Topography Drumlin landscape
Major soil types Poorly drained mineral soils and peats
Main land use Agricultural grassland
Human settlement Dispersed single dwellings
Human population 725
Cattle numbers 5636
Sheep numbers 2128
Land-use in the catchment is dominated by agriculture. A study of agricultural waste
management in Co. Monaghan was carried out by Teagasc (1994). This included desktop
analysis and a survey of 400 farms throughout the county to project the waste budget for the
county for the next twenty years and to make recommendations as to how it should be
managed. The survey showed that 90 % of agricultural production in the county was grassland
based, involving cattle or sheep, 6 % was poultry based, 3.5 % was mushroom based and 0.5 %
was swine based. The survey estimated the phosphorus (P) surplus for the county to be 28
kg/ha. This was accounted for by the use of both animal and chemical fertilisers. The study
further highlighted the risks to water courses in the county due to the poor assimilative capacity
of the soil in the region, the drumlin topography and attendant steep slopes and the high annual
rainfall. Along with the dominance of agriculture in the catchment there is the issue of one-off
housing and the attendant wastewater treatment problems. Although revised recommendations
were introduced to regulate the standard of wastewater treatment in single housing (EPA 2000),
many of the houses in the catchment pre-date the introduction of these measures. There is a
cluster of twenty five houses in the village of Oram which are serviced by a reed bed filtration
system which then discharges into a drain a few hundred metres upstream of the lake. Industry
in the catchment consists of two large truck stops and an international haulage company.
27
Prior to the commencement of the current study, the group water scheme (GWS) at Churchill
and Oram had highlighted concerns over the quality of water in the lake at Milltown. Periodic
algal blooms and persistent high levels of phosphorus in the water required extensive and
expensive treatment to render the water potable. In 2004 the GWS committee commissioned a
report on the quality of water in the catchment (Quirke 2004). This work was carried out in
May 2004 by private consultants and highlighted elevated phosphorus levels in Milltown Lake
(presumed due to poor water quality of inflowing streams) and also reported problems within
the catchment in relation to hydrocarbon discharges and illegal dumping. Of the eighteen sites
sampled in the catchment, seven were designated Q3 (moderately polluted), seven were
designated Q4 (unpolluted) and four were designated Q4-5 (unpolluted).
The Environmental Protection Agency (EPA) carries out sampling in the Milltown Lake
catchment as part of their monitoring program on the larger River Fane catchment. Biological
sampling has been carried out every three years on the middle tributary since 1994 and
chemical sampling is carried out every month of every second year on two sites, one being the
biological sampling site and the other being the location of a staff gauge at the final inflow to
the lake. The Q-value assigned by the EPA to this site in 2003 was Q3-4 (moderately polluted),
but this had improved by 2006 when it was classed as Q4 (unpolluted). The inflow site was also
sampled as part of the biological program during 1990 when it was classed as Q4 (Catherine
Bradley, EPA, pers.comm.). Chemical monitoring of streams in the catchment by the EPA
began in 2004 in response to the introduction of the Phosphorus Regulations (Minister for the
Environment and Local Government 1998). Sampling is carried out every second year and
comprises monthly sampling covering the following physico-chemical parameters; temperature,
conductivity, pH, true colour, dissolved oxygen (DO), biochemical oxygen demand (BOD),
total reactive phosphorus (TRP), total ammonia, total oxidised nitrogen (TON) and chloride and
in 2004, total P (TP) (Table 2.2). The assessments found in both 2004 and 2006 that the
orthophosphate concentrations classed the stream as slightly polluted, DO concentrations were
lowest in June and July of both years and in October and November of 2004 there were
incidents of ‗gross‘ nutrient pollution (Quinn 2004; Quinn 2007).
28
Table 2.2 Summary statistics for physico-chemical data at Environmental Protection Agency monitored sites in the Milltown Lake
catchment in 2004 and 2006, n=12 (Quinn 2004; Quinn 2007).
Year Site
Code
Statistic Temp.
ºC
Cond.
µS/cm
pH True
Colour
DO
%SAT
BOD
mg/L
O2
TRP
mg/L P
Ammonia
mg/L N
TON
mg/L
TP
mg/L
2006 0040 Min. 6.4 216 7.4 68 61 <1.5 <0.02 <0.03 0.52 -
Med. 10.7 242 7.6 79 94 2 0.03 0.07 1.48 -
Max. 20.5 456 7.7 103 98 8.3 0.06 0.65 2.05 -
0080 Min. 6.9 204 7.3 51 92 <1.5 <0.02 <0.03 0.11 -
Med. 12.9 227 7.6 75 98 2.5 0.03 0.05 1.8 -
Max. 21.7 387 8.1 93 120 7.7 0.06 0.08 2.71 -
2004 0040 Min. 4.9 188 7.3 60 70 <1.5 0.02 0.03 0.47 0.05
Med. 9.3 242 7.8 86 91 2 0.04 0.06 1.06 0.07
Max. 15.7 350 8 162 99 >7.1 0.3 3.81 1.78 0.46
0080 Min. 3 167 7.4 46 72 <1.5 0.02 0.03 <0.05 0.04
Med. 9.4 224 7.8 70 96 <1.65 0.04 0.05 1.4 0.65
Max. 16.8 332 8.2 155 100 6.6 0.14 0.21 2.32 0.29
29
2.2 Sampling Sites
The chemical and biological sampling sites on the streams were chosen using the following
criteria; some must represent the headwater of each tributary, show upstream and downstream
effects of each tributary as it joined the main channel and have ease of access to site (including
landowner‘s permission). Once these criteria had been met sampling sites were located on
stretches which were as physically homogenous as possible throughout their length, sites also
needed to contain riffles to facilitate the invertebrate sampling methodology outlined in Chapter
5. Sites at which livestock accessed the watercourse for drinking water were avoided where
possible. Locations of sites selected are presented in Figure 2.3 and photographs are shown in
Plates 1-4.
Sampling for macrophytes was undertaken over a 100m length; macroinvertebrate samples
were taken from only the riffle sections within that length. As the proportion of riffle varied
from site to site the percentages of riffle at each site are given in Table 2.6. Samples and
recordings for water chemistry were taken mid-stream, within the dominant flow type. Samples
for sediment chemistry were taken within the 100m survey length where substrate and the
presence or absence of macrophytes allowed (samples could not be collected of larger substrate
particle sizes and both vegetated and unvegetated sediment were sampled where it was possible
to do so). Replicates of sediment samples were taken from as wide a range throughout the 100m
length as possible in an attempt to represent the potential heterogeneity in the stream bed over
that distance. Detailed methodologies relating to each of the elements are to be found in the
methods sections of the relevant chapters.
During sampling the physical characteristics of the sites were recorded, namely wetted width
(m), depth (m), particle size, percentage flow type, degree of shading, presence or absence of a
bridge at the site, bed stability and water clarity. Substrate size was assessed visually
throughout the length of the site, according to the substrate particle sizes in Table 2.3. These
were then ranked 1 to 5 in order of abundance. The three flow types recorded are described in
Table 2.4. The degree of shading was also visually assessed, a three point scale of high,
medium and low was used which corresponded to the categories used in the Mean Trophic
30
Rank (MTR) (Holmes et al. 1999), detailed in Table 2.5. Bed stability and water clarity were
also assessed following the MTR method. Slope was calculated and stream order assigned using
the relevant OS map. Slope was calculated as the difference in altitude between contours on
either side of the site, divided by the distance between the two contours (Holmes et al. 1999).
Stream order was assigned using the Strahler method (Horton 1945), based on the Milltown
Lake catchment.
Table 2.3 Stream substrate particle size categories based on the Wentworth Scale (Modified by
Cummins (1962)).
Particle Type Diameter (mm)
Boulder >256
Cobble 64-256
Gravel/Pebble 2-64
Sand 0.0625-2
Silt/Clay <0.0625
Table 2.4 Flow type categories based recorded.
Flow Type Description
Riffle Upstream facing wavelets which are not
broken
Glides Perceptible downstream movement is
smooth (no eddies)
Pools No net downstream flow
Table 2.5 Stream shade categories based on the Mean Trophic Rank methodology (Holmes et
al. 1999).
Shade Categories MTR Shade Categories Description
Low None No shading
Medium Broken Some direct sunlight hits the
water when the sun is directly
overhead
High Dense 5% or less of the site receives
direct sunlight when the sun is
directly overhead
31
Figure 2.3 Location of sampling sites in the Milltown Lake catchment (sites in red were
surveyed for all parameters by the author, sites in green were surveyed for macrophytes by the
author and for macroinvertebrates by Niamh Sweeney, NSPPP). EPA sampling site is located
at GO5
32
Most sites in the catchment were of stream order 1, seven sites were on a 2nd
order stream
(TV and the lower reaches of TH) and two sites were on a 3rd
order stream (Table 2.6).
The mean wetted width of sites sampled was 1.61 m and the mean depth was 0.17 m. The
most common substrate type at the majority of sites was cobble. There were three flow
types recorded in the catchment; riffle, glide and pool. Due to access issues some sites
were either up or downstream of a bridge. Bed stability at all sites but one (D1) was
classed as ‗stable‘ and water clarity at all sites was classed as ‗clear‘.
33
Table 2.6 Site physical characteristics for the Milltown Lake catchment. Substrate particle type indicated refers to that which was
ranked highest in order of dominance. Presence of a bridge is indicated by 1 and absence by 0
Site Width (m) Depth (m) Area (m2) Substrate % Riffle % Glide % Pool Shading Bridge Order Slope(m/km)
D1 4.00 0.27 400 Sand 20 80 0 Medium 1 3 5.56
D2 4.00 0.25 400 Boulder 10 80 10 Medium 1 3 5.56
TV1 1.50 0.20 150 Boulder 60 40 0 Medium 1 1 6.25
TV2 0.90 0.10 90 Boulder 40 60 0 Medium 0 1 50.00
TV3 2.00 0.20 200 Cobble 20 80 0 Medium 1 2 20.00
TV4 1.50 0.15 150 Boulder 40 60 0 Medium 1 2 50.00
TV5 2.00 0.17 200 Boulder 10 90 0 Medium 1 2 10.00
TV6 1.87 0.11 187 Cobble 70 30 0 Medium 0 2 8.33
TV7 2.50 0.20 250 Bedrock 40 60 0 High 1 2 8.33
GO1 1.00 0.10 100 Gravel/pebble 20 40 40 High 0 1 5.56
GO2 1.12 0.12 112 Cobble 70 30 0 Medium 0 1 14.29
GO3 0.75 0.16 75 Cobble 30 70 0 Low 0 1 50.00
GO4 1.30 0.30 130 Cobble 60 40 0 Medium 1 1 50.00
GO5 2.30 0.16 230 Cobble 60 40 0 High 1 1 7.69
GO6 1.00 0.16 100 Gravel/pebble 30 70 0 Low 1 1 3.45
TH1 1.50 0.15 150 Cobble 60 40 0 High 1 1 12.50
TH2 1.50 0.10 150 Cobble 40 60 0 High 1 1 12.50
TH3 1.80 0.13 180 Cobble 90 10 0 High 1 1 33.33
TH4 1.50 0.15 150 Cobble 60 40 0 Low 0 1 16.67
TH5 1.25 0.10 125 Cobble 20 80 0 Low 1 2 14.29
TH6 1.30 0.13 130 Cobble 60 40 0 Low 1 2 7.69
MA2 1.00 0.50 100 Gravel/pebble 10 90 0 High 0 1 50.00
MA4 0.87 0.17 87 Cobble 80 20 0 Medium 1 1 20.00
DG1 1.00 0.20 100 Cobble 90 10 0 Medium 0 1 20.00
DG2 2.00 0.15 200 Gravel/pebble 20 70 10 High 0 1 16.67
CN1 0.75 0.10 75 Cobble 20 80 0 High 1 1 14.29
34
TV1 TV2
TV3 TV4
TV5 TV6 TV7
Plate 1 Sites of the TV tributary in the Milltown Lake catchment.
35
GO1 GO2 GO3
GO4 GO5 GO6
Plate 2 Sites of the GO tributary in the Milltown Lake catchment.
36
TH1 TH2 TH3
TH4 TH5 TH6
Plate 3 Sites of the TH tributary in the Milltown Lake catchment.
37
DG1
CN1
DG2
MA2
MA4
D1 D2
Plate 4 Selected sites in the Milltown Lake Catchment.
38
Chapter 3 The Macrophyte Flora of Milltown Lake Catchment
__________________________________________________________________
3.1 Introduction
Country-wide surveys of the aquatic macrophyte communities in Ireland are few and most have
been carried out in more recent years to provide information on the water quality of particular
aquatic habitats. Work on the rivers of Ireland has mostly been carried out by Inland Fisheries
Ireland (IFI) and the National Parks and Wildlife Service (NPWS). The work carried out by the
IFI includes surveys on the macrophyte communities of many of Ireland‘s larger rivers and
canals. These surveys have mostly been concerned with the spread and management of rare,
weed or alien invasive species (Caffrey & Monahan 1999; Caffrey & Monahan 2006). The
work carried out by the NPWS included a survey of the vegetation of Irish rivers (Heuff 1984b)
and proposed phytosociological groupings for each of the river types surveyed. Sites were typed
according to their altitude and geology. Part of the emphasis of the project was on recording
species data for conservation purposes and as such, the type of sites selected tended to be in
areas of high conservation interest (e.g. Connemara and the Burren) and were focused on larger
sites.
Most recent work has been carried out on macrophytes in Ireland in light of the requirement of
the Water Framework Directive (WFD) to include macrophytes in monitoring programs for
surface waters (Dodkins et al. 2005a; Kelly-Quinn et al. 2005). Dodkins et al. (2005a)
developed an ecological quality assessment method based on metrics derived from a data set of
rivers in Northern Ireland. Kelly-Quinn et al. (2005) recorded the macrophyte flora of river sites
throughout the Republic of Ireland to provide information on reference conditions. The data
were used for typological classification of rivers within the island of Ireland and water quality
assessments of river macrophyte communities are currently being carried out by the
Environmental Protection Agency (EPA).
39
The sites in this study are of a kind which has often been excluded from other surveys of Irish
rivers as they are small (WRBD 2005), not of reference condition (Kelly-Quinn et al. 2005) or
of conservation interest (Heuff 1984b). However, theses sites and communities are subject to
considerable pollution pressure from agricultural and domestic sources (Arnscheidt et al. 2007)
and so provide an opportunity for study into the relationship between their macrophyte
communities and pollution pressures at the sub-catchment level. Furthermore, these sites are
located on the Irish drumlin belt. This area is comprised of many small hills with poorly drained
creating an intricate drainage pattern of small first and second order streams and brooks feeding
the many lakes that lie in the depressions between hills. As such these sites represent neither the
small, high altitude mountain streams nor the wide, deep lowland streams differentiated by
various studies on macrophytes (Barendregt & Bio 2003; Baattrup-Pedersen et al. 2006;
Scarlett & O‘Hare 2006). Aspects of the drumlin landscape which may impact macrophyte
communities include the proportion of peat in the catchment (King & Champ 2000), stream
gradient/slope (Moles et al. 2003), flashy hydrology (Lake 2000; McGarrigle 2001) and size
(Baattrup-Pedersen et al. 2006).
Temporal variation in macrophyte cover throughout the growing season is related to several
factors. These include flow (Wilcock et al. 2004; Baldy et al. 2007; Feijoó & Lombardo 2007;
Franklin et al. 2008), shading (Wilcock et al. 2004; Brabec & Szoszkiewicz 2006; Staniszewski
et al. 2006), suitability of substrate (Feijoó & Lombardo 2007; Kuhar et al. 2007) and the
particular growth habits of the species in question (Butcher 1933). It can also be related to the
availability of a limiting nutrient at a site (Dawson et al. 1999). Sampling methodologies often
suggest that sampling be carried out only once in the growing period and at the same time of the
year on repeat surveys as this is a source of variability which may impact the usefulness of
macrophyte assessments (Brabec & Szoszkiewicz 2006; Staniszewski et al. 2006).
Studies assessing the relationship between macrophyte communities and differing physical and
chemical conditions have been carried out in response to the WFD. These studies have been
carried out at both the larger, pan-European scale and at more regional and local scales. As the
scale of a study changes, so too do the important variables explaining variation in the
macrophyte community (Barendregt & Bio 2003). Large scale projects have tended to highlight
40
the relationship between macrophyte communities and geology, altitude and alkalinity (Palmer
et al. 1992; Riis et al. 2000; Scarlett & O‘Hare 2006). Studies focused on a regional scale,
where variations in altitude and geology may be less marked, have shown a greater relationship
between site physico-chemical characteristics and macrophyte community structure.
Conductivity and nitrogen (as nitrate or ammonium) are often highlighted at this scale (Feijoó
& Lombardo 2007; Dodkins et al. 2005a; Thiebaut et al. 2002) as are stream size (Triest 2006;
Heino et al. 2005) and land use (Oťaheľová et al. 2007). Kuhar et al. (2007), working on nine
rivers in north-eastern Slovenia found that substrate characteristics were pertinent factors
shaping the community, suggesting the importance of local scale physical characteristics.
Both bank and aquatic species have been used in some analyses of macrophyte responses to
nutrient inputs (Kelly-Quinn et al. 2005; Matson 2006; Skoulikidis et al. 2009) and aquatic
species only in others (Dawson et al. 1999; Dawson & Szoszkiewicz 1999; Dodkins et al.
2005a). Surveys which have included bank species are few, although stream and river banks
can have their own characteristic species assemblages (Watson 1981). However, it is uncertain
how much influence the stream characteristics and quality will have on those assemblages.
Bank species are subject to particular conditions due to their proximity to the water e.g. wet
soils and in some cases intermittent flooding. It may be the case that the bank species show
greater agreement with water quality parameters during high flow events. They are also subject
to the management practices in the riparian area and can accumulate significant concentrations
of nutrient as run off from agriculture (Kronvang et al. 2005). The extent of this can vary with
agricultural practices and with vegetation types (Osborne & Kovacic 1993). Emergent riparian
vegetation has also been shown to improve the retention of ammonium in streams, which may
be related to the shading they provide (Wilcock et al. 2004). The relative importance of both the
in-stream conditions and the riparian management practices will also vary, but it is uncertain to
what degree. It has been recommended that bank species be recorded for further development
of Canonical Based correspondence Analysis Score (CBAS) (NSSHARE 2008). This water
quality metric was developed based on the relationships between aquatic taxa and selected
parameters of water quality. However, their potential future inclusion was related to the
importance of bank species to the ecology of rivers (Kelly-Quinn et al. 2005).
41
3.2 Aims
1. To describe the macrophyte flora of a small drumlin catchment with regard to relevant,
comparable macrophyte typologies
2. To assess the temporal variability in macrophyte taxon richness and abundance in the
catchment both within and between years
3. To provide a classification of sites on the Milltown lake catchment based on the
macrophyte community
4. To highlight macrophyte community responses to physical and chemical conditions in
the Milltown Lake catchment
5. To assess the ability of the bank species to differentiate between sites and to respond to
physico-chemical conditions at those sites by comparison with the aquatic species
42
3.3 Methods
3.3.1 Macrophyte Sampling and Analysis Methodology
Macrophyte surveys were carried out three times at each sampling site in the catchment (Figure
2.3), twice during the 2006 growing season (June and September) and once during June 2007.
Surveys were carried out within two weeks to minimise temporal variation. Survey
methodologies followed those of the Mean Trophic Rank (MTR) (Holmes et al. 1999). Sites
were 100 m in length and sampling was carried out by wading the length of the site in a zig-zag
manner. The macrophyte species present were recorded, their percentage cover estimated and
assigned to an abundance class on a 9 point scale (Table 3.1). Macrophytes growing in the
channel (aquatic) and those growing associated with the channel, but only submerged between
50 % and 85 % of the year (bank) were recorded. Aquatic macrophyte abundance was recorded
as a percentage of the channel. Bank species were recorded as a percentage of the two strips of
land along either side of the channel that was submerged between 50 % and 85 % of the year.
The area sampled for bank species varied from site to site according to the conditions at the site.
If species were growing in both the channel and on the bank they were recorded as aquatic, not
both bank and aquatic. Species which were attached to bridges were not recorded as bridges
were not present at all the sites sampled. The MTR states that species attached to structures
such as bridges should only be recorded if there are bridges at the other sites being compared
(Holmes et al. 1999).
Macrophytes were identified in the field where possible but bryophytes and those that could not
be easily identified in the field were brought back to the laboratory for microscopic
examination. Identification was carried out using British Water Plants (Haslam et al. 1975), An
Irish Flora (Webb et al. 1996), and Dr Nigel Holmes‘ macrophyte training handbook.
Bryophytes were identified using British Mosses and Liverworts (Watson 1981). Data are
presented in Appendix 1.
Training in the field identification of macrophytes was undertaken before the first sampling
period by attendance at the University College London/Centre for Ecology and Hydrology
macrophyte identification course given by Dr Iwan Jones. Onsite guidance on sampling
43
methods was provided by Dr Ronan Matson, University College Dublin. Further training in
field identification was undertaken in May 2007 with the EPA macrophyte identification course
given by Dr. Holmes. Identification of bryophyte samples was aided by Dr. Matson both in the
laboratory in June 2006 and by identification of voucher specimens from 2007. Surveys were
always carried out by the same surveyor, thereby ensuring consistency, even in potential errors.
Table 3.1 Abundance classes for macrophyte percentage cover values
Percentage Abundance Classes Coded Category Value
<0.1% 1
0.1-1% 2
1-2.5% 3
2.5-5% 4
5-10% 5
10-25% 6
25-50% 7
50-75% 8
>75% 9
3.3.2 Physico-chemical Sampling and Analysis Methodology
Physico-chemical sampling was carried out at all sites in the catchment monthly and is
summarised in Table 3.2. Data are presented in Appendix 2. Dissolved oxygen (% Saturation),
temperature, pH, conductivity and Total Dissolved Solids (TDS) were measured
electrometrically in the field using the YSI 556 MPS multiparameter probe. Calibration was
carried out daily during each sampling period. DO calibration was carried out using a water-
saturated air calibration. A 2 point pH calibration was carried out using buffer solutions of pH 4
and 7. Conductivity calibration was carried out using a 1000 µS/cm standard. Water samples
were collected from each site in acid washed 1L polythene bottles which were rinsed with river
water before sample collection. Samples were taken within the 100 m length of the survey site
and repeat samples were taken at the same location.
Total Reactive Phosphorus (TRP) analysis was carried out on unfiltered samples
colorimetrically, following the molybdate method of Murphy & Riley (1962), using a standard
curve in the range 0.01 – 0.2 mg/L. Standards were made using analytical grade potassium
44
dihydrogen phosphate. After colour development, absorbance was read on a Gensys 6
spectrophotometer at 680 nm with a 5 cm cell, providing a Limit Of Detection (LOD) of 0.005
mg/L.
Samples were analysed for total ammonia colorimetrically, following the hypochlorite method
(Solorzano 1969), using a standard curve in the range 0.01 – 0.6 mg/L. Standards were made
using analytical grade ammonium chloride. After colour development, absorbance was read on
a Gensys 6 spectrophotometer at 635nm with a 5cm cell, providing an LOD of 0.01 mg/L.
Samples were analysed for alkalinity at each of the three biological sampling periods by
potentiometric titration to an end point of pH 4.5 with normalised 0.01 M sulphuric acid
(APHA 1995). No samples required the low alkalinity Gran Titration method.
Samples were also collected concurrently for analysis of Five Day Biochemical Oxygen
Demand (BOD). Samples were collected under the surface of the water in stoppered, glass
bottles and taken back to the laboratory for incubation and analysis. Two samples were
collected per site, one sample was ‗fixed‘ by addition of 1ml alkali azide and 1 ml MnSO4 and
was used to measure the initial DO concentration, while the other was incubated at 21 ºC for
five days before analysis. The iodiametric technique with azide modification using titration
with 0.025 M Na2S2O3 (APHA 1995) was used to measure DO in both samples.
Table 3.2 Physico-chemical parameters monitored and methods of measurement
Parameter Abbreviation Units Method
pH pH pH units Electrometrically
Alkalinity Alk. mg/L CaCO3 Titration
Conductivity Cond. µS/cm @ 20ºC Electrometrically
Total Dissolved Solids TDS g/L Calculation
Temperature Temp. ºC Thermometer
Total Reactive Phosphorus TRP mg/L P Colorimetrically
Ammonium NH4 mg/L N Colorimetrically
Dissolved Oxygen DO % SAT % Electrometrically
Dissolved Oxygen DO mg/L mg/L O2 Titration
Biochemical Oxygen Demand BOD mg/L O2 Titration
45
3.3.3 Statistical Analysis
3.3.3.1 Temporal Variation
Temporal variation in the macrophyte community was explored using two non-parametric tests.
Both tests were carried out on a dataset of Bank macrophytes and a dataset of Aquatic
macrophytes (Table 3.3).
1. The Kruskal Wallis test was carried out on both the number of taxa present at each sampling
occasion and on a measure of macrophyte abundance provided by summing the coded category
values (Table 3.1) separately. This was carried out using the R statistical package
(http://www.r-project.org/). The hypothesis tested was as laid out below:
H0: There is no difference in either the macrophyte abundance or taxon richness between
sampling periods
H1: Macrophyte abundance or taxon richness is different in at least one of the sampling periods
2. A RELATE analysis was carried out on a similarity matrix (Bray Curtis distance) of all sites
based on abundance of all the species in the dataset using the Primer v6 statistical package
(Clarke & Gorley 2006). This test was carried out in a pairwise manner while the Kruskal
Wallis test compared all three seasons simultaneously. The hypothesis tested was as laid out
below:
H0: There is no difference between macrophyte species composition and abundance between
sampling periods
H1: Macrophyte species composition and abundance is different between the sampling periods
46
3.3.3.2 Classification Analyses
The similarity matrices calculated by Primer for the RELATE analysis above were also used for
hierarchical clustering analysis (CLUSTER) using group-average linking on the macrophyte
abundance data. This was carried out to provide a classification of the sites based on the
macrophyte community. The distribution of macrophyte abundance was not expected to be
normal, but as the percentage abundance categories used in the survey methodology is a type of
transformation in itself (Clarke & Gorley 2006; Leps & Smilauer 2003), no further treatment of
the data was used. Analyses were carried out on both the Bank macrophyte and Aquatic
macrophyte datasets separately. In the dendrogram derived from this analysis, sites were
grouped or ‗clustered‘ together, showing which sites were alike based on the distances
calculated between their macrophyte assemblages.
The end groups created by the CLUSTER analysis were validated using a number of different
methods. The SIMPROF permutation test was run at every node of the dendrogram produced to
test for statistically significant internal structure in the group being subdivided. Divisions of the
dendrogram within clusters that had no significant internal structure were not seen as creating
valid end groups. Classifications should not be tested by the same dataset from which they were
derived as this creates a circular argument where the same dataset is analysed twice with the
same aim (Clarke & Gorley 2006; Perrin et al. 2006). Instead, the end groups were further
validated by using an independent dataset of physico-chemical parameters measured for each
cluster group. Where both analyses agree, confidence in the classification is increased. Kruskal
Wallis tests were used to test for significant differences between physical and physico-chemical
characteristics recorded for each cluster group. This analysis was carried out in SPSS v. 16.
Indicator species analysis (INDVAL) was carried out using the labdsv library in the R program
(http://cran.r-project.org/).
3.3.3.3 Community Responses to Physico-chemical Conditions
Ordination analysis was carried out using the Canoco 4.5 statistics package to highlight
macrophyte community responses to physical and chemical conditions in the Milltown Lake
catchment. This was carried using a dataset of the median value from 10 samples taken over the
47
year from May 2006 to May 2007 for each site (except BOD where only 6 values were
available and for alkalinity where only 3 were available). This was compared with the May
2007 macrophyte data. As is expected of physico-chemical data, Kolmogorov Smirnov tests
(SPSS v. 16) showed that they did not display a normal distribution (Helsel & Hirsch 2002;
Clarke & Gorley 2006) and so a log(x) transformation was applied. In Canoco, resemblance
matrices were calculated for both the macrophyte and physico-chemical data based on
Euclidean distances. As the physico-chemical dataset comprised parameters measured on
different scales, the data were standardised by bringing their means to 0 and their variances to
1. Both the physico-chemical variables and the biological abundance data were then used to
carry out Detrended Correspondence Analysis (DCA). This was used to assess whether linear or
unimodal methods were best suited to the data. Where DCA gradient lengths are less than 3 (i.e.
the biological data shows a small gradient in beta diversity), linear methods should be selected.
Constrained ordination was then carried out, this type of analysis searches for combinations of
the supplied environmental data to explain variation in the biological community. ReDundancy
Analysis (RDA) is the linear version of constrained analysis while Canonical Correspondence
Analysis (CCA) is the unimodal version. The significance of the ordinations was assessed using
a Monte Carlo permutation.
48
3.4 Results
3.4.1 The Macrophyte Flora of the Milltown Lake Catchment
There were 36 macrophyte taxa recorded in the Milltown Lake Catchment and one grouping of
filamentous algae (Table 3.3). Eleven of those taxa recorded were bryophytes (3 Hepaticae, 8
Musci) and the rest were higher plants. The most commonly recorded species in the Bank
macrophyte dataset was J. effusus, which was found at 24 of the 26 sites surveyed (92.3 %).
The least commonly recorded species was H. vulgaris, which was only found at one site, MA4
(3.8 %) (Appendix 1). The average number of taxa recorded in the Bank macrophyte dataset per
site for all 3 sampling periods was 6.8. The highest number of taxa recorded at any one site was
10 at GO6, TH6 and MA2 (Figure 3.1a). The lowest number of taxa recorded at any one site
was 2 at site TV6 in each of the sampling periods (Figure 3.1a). Rooted higher plants were the
most abundant plant grouping at the majority of sites in each of the 3 sampling periods (Figure
3.2). Exceptions to this were D1, TV4 and TV6 in June 2006.
The most commonly recorded species in the Aquatic macrophyte dataset was F. antipyretica,
which was found at 22 of the 26 sites surveyed (84.6 %). The least commonly recorded aquatic
species in the catchment were T. latifolia and E. canadensis, which were only recorded at one
site, TV1 (3.8 %) (Appendix 1). The average number of aquatic taxa per site, for all 3 sampling
periods was 6.4. The highest number of aquatic taxa recorded at any one site was 12 at GO6 in
June 2006 and at TV1 in September 2006, while the lowest was 2 at MA2 in June of 2007
(Figure 3.1c). The Bryophyta were the most abundant plant grouping at the majority of sites in
each of the 3 sampling periods (Figure 3.3).
It was noted throughout each of the surveys that no site was choked with macrophyte growth
and that bare substrate was dominant at most sites. At site GO3, where macrophyte growth did
cover the majority of the channel it was the liverwort, C. polyanthos, which was dominant.
49
Table 3.3 Taxa list for the Milltown Lake catchment with key to codes used in statistical
analyses
Species Name Species Code
Aquatic Species
Alisma plantago aquatica L. Alpa
Brachythecium rivulare Schimp. Brri
Callitriche obtusangula Le Gall Caob
Chiloscyphus polyanthos (L.) Dumort Chpo
Elodea canadensis Michx Elca
Filamentous algae Fila
Fontinalis antipyretica Hedw. Foan
Glyceria maxima (Hartm.) Holmberg Glma
Hygroambylestegium fluviatile (Hedw.) Loeske Amfl
Hygrohypnum luridum (Hedw.) Jenn Hylu
Lemanea fluviatilis L. Lefl
Lemna minor L. Lemi
Leptodictyum riparium (Hedw.) Warnst Amri
Oenanthe crocata L. Oecr
Pellia endiviifolia (Dicks.) Dumort Peen
Platyhypnidium riparioides (Hedw.) Dixon Rhyn
Rorippa nasturtium aquaticum agg. Rona
Sparganium emersum Rehm. Spem
Typha latifolia L. Tyla
Bank Species
Angelica sylvestris L. Ansy
Caltha palustris L. Capa
Carex spp. Casp
Equisetum arvense L. Eqar
Fissidens spp. Fisp
Filipendula ulmaria (L.) Maxim. Fiul
Hydrocotyle vulgaris L. Hyvu
Iris pseudacorus L. Irps
Juncus effusus L. Juef
Lunularia cruciata (L.) Lucr
Myosotis spp. Mysp
Persicaria hydropiper L. Pehy
Phalaris arundinacea L. Phar
Ranunculus flammula L. Rafl
Stachys spp. Stsp
Thamnobryum alopecurum (Hedw.) Gangulee Thal
Veronica anagallis-aquatica L. Veaa
Veronica beccabunga L. Vebe
50
a)
0
2
4
6
8
10
12
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7GO1GO2
GO3GO4GO5GO6
TH1
TH2
TH3
TH4
TH5
TH6M
A2
MA4
DG
1DG
2CN1
Site Code
No
. S
pecie
s
Jun-06
Sep-06
Jun-07
b)
0
5
10
15
20
25
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7GO1GO2
GO3GO4GO5GO6
TH1
TH2
TH3
TH4
TH5
TH6M
A2
MA4
DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Jun-06
Sep-06
Jun-07
c)
0
2
4
6
8
10
12
14
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7GO1GO2
GO3GO4GO5GO6
TH1
TH2
TH3
TH4
TH5
TH6M
A2
MA4
DG
1DG
2CN1
Site Code
No
. S
pecie
s
Jun-06
Sep-06
Jun-06
d)
0
5
10
15
20
25
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7GO1GO2
GO3GO4GO5GO6
TH1
TH2
TH3
TH4
TH5
TH6M
A2
MA4
DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Jun-06
Sep-06
Jun-07
Figure 3.1 Bar charts of a) number of species and b) summed abundance classes for the Bank macrophyte dataset and c)
number of species and d) summed abundance classes for the Aquatic macrophyte dataset for June 2006, September 2006 and
June 2007
51
a)
0%
20%
40%
60%
80%
100%
D1
D2
TV1
TV2TV
3TV
4TV
5TV
6TV
7GO1GO2GO3GO5GO6
TH1TH
2TH
3TH
4TH
5TH
6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Bryophyta
Higher Plants
b)
0%
20%
40%
60%
80%
100%
D1
D2TV
1TV
2TV
3TV
4TV
5TV
6TV
7GO1GO2GO3GO4GO5GO6
TH1TH
2TH
3TH
4TH
5TH
6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Bryophyta
Higher Plants
c)
0%
20%
40%
60%
80%
100%
D1
D2
TV1TV
2TV
3TV
4TV
5TV
6TV
7GO1GO2GO3GO4GO5GO6
TH1TH
2TH
3TH
4TH
5TH
6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Bryophyta
Higher plants
Figure 3.2 Percentage abundances of Bryophytes and Higher Plants for the Bank macrophyte dataset at each site
for a) June 2006, b) September 2006 and c) June 2007
52
a)
0%
20%
40%
60%
80%
100%
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7GO1GO2GO3GO5GO6
TH1
TH2
TH3
TH4
TH5
TH6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce Bryophyta
Algae
Floating
Rooted
b)
0%
20%
40%
60%
80%
100%
D1
D2
TV1
TV2TV
3TV
4TV
5TV
6TV
7GO1GO2GO3GO4GO5GO6
TH1
TH2TH
3TH
4TH
5TH
6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce Bryophyta
Algae
Floating
Rooted
c)
0%
20%
40%
60%
80%
100%
D1
D2
TV1
TV2TV
3TV
4TV
5TV
6TV
7GO1GO2GO3GO4GO5GO6
TH1
TH2TH
3TH
4TH
5TH
6M
A2M
A4DG
1DG
2CN1
Site Code
Ab
un
dan
ce
Bryophyta
Floating
Rooted
Figure 3.3 Percentage abundances of Bryophytes, Algae and Rooted and Free Floating Higher Plants for the
Aquatic macrophyte dataset at each site for a) June 2006, b) September 2006 and c) June 2007
53
3.4.2 Temporal Differences in Taxon Richness and Abundance
There were 18 Bank macrophyte taxa recorded in the catchment over the 3 sampling periods
(Appendix 1). There were 18 Aquatic macrophyte taxa recorded in the catchment in both June
2006 and September 2006 (Appendix 1). This decreased to 16 in June 2007, mostly due to the
absence of algal species. There was a net decrease in the number of species recorded for the
Bank macrophyte dataset over the study period at the majority of sites (16). There was a net
increase in the number of species recorded at 3 sites (D2, TV2, and GO2) (Figure 3.1a) and a
net decrease in the number of species recorded at 6 sites (GO1, GO3, GO4, GO6, TH6 and
MA2) (Figure 3.1a). There was no change in the number of species recorded at 17 of the
sampling sites. In the Aquatic macrophyte dataset there was a net decrease in the number of
species recorded at the majority of sites (14). There was no change in the number of species
recorded at 10 of the sampling sites. There was a net increase in the number of species recorded
at 2 of the sites (D2 and GO2) (Figure 3.1c).
There was a net decrease in macrophyte abundance recorded at 9 sites for the Bank macrophyte
dataset over the entire study period. There was a net increase at 13 sites and no net change at 4
sites (D2, TV2, TV6 and TH1) (Figure 3.1b). At 7 sites in the catchment there was an increase
in macrophyte abundance from June 2006 to September 2006 and a corresponding decrease in
June 2007. There was a net increase in abundance over the entire study period at one of those
sites (MA2) and a net decrease at 4 of those sites (TV5, GO3, GO6 and DG2). Ten sites showed
a net decrease in abundance recorded for the Aquatic macrophyte dataset over the entire study
period. Ten sites showed a net increase and 6 sites showed no net change (Figure 3.1d). Eleven
sites in the catchment showed an increase in macrophyte abundance from June 2006 to
September 2006 with a corresponding decrease in June 2007. Of those sites, 2 showed a net
increase in abundance over the entire study period (TH1, TH4) and 3 showed a net decrease
(D1, GO1 and MA2).
Kruskal Wallis test showed that the differences in taxon richness recorded in each of the three
sampling periods were not significant for either the Bank macrophyte (χ²=0.067, p=0.967) or
Aquatic macrophyte (χ²=2.65, p=0.266) datasets. There was also no significant difference in
taxon abundances between seasons for either the Bank macrophyte (χ²=0.6127, p=0.736) or
54
Aquatic macrophyte (χ²=0.582, p=0.787) datasets. Taxon richness and abundances are
summarised in Figure 3.4
a)
b)
c)
d)
Figure 3.4 Boxplots of the number of species in a) the Bank macrophyte dataset and b) the
Aquatic dataset and boxplots of summed abundance classes for c) the Bank macrophyte dataset
and d) the Aquatic macrophyte dataset
55
The RELATE test for each of the three analyses (June 2006 v. Sept 2006, June 2006 v. June
2007 and Sept 2006 v June 2007) showed that there was a high level of concordance between
the three sampling periods (Table 3.4). This suggests that the temporal variation between the
sites was low and is in agreement with the Kruskal Wallis test above. Inter-seasonal
concordance was highest for both datasets (June 2006 v. Sept 2006). Inter-annual concordance
was lowest for both datasets between September 2006 and June 2007 (ρ =0.822, p=0.01 and ρ
=0.753, p=0.01).
Table 3.4 Summary statistics for RELATE analyses on the Bank macrophyte and Aquatic
macrophyte datasets between each of the sampling periods
Bank Macrophytes Aquatic Macrophytes
ρ p ρ p
June 06 v Sept 06 0.920 0.01 0.860 0.01
June 06 v June 07 0.827 0.01 0.754 0.01
Sept 06 v June 07 0.822 0.01 0.753 0.01
56
3.4.3 Classification of Sites on the Milltown Lake Catchment
Hierarchical clustering analysis was first carried out on data from each of the 3 sampling
periods separately. The SIMPROF routine was carried out concurrently to test for significant
structure in the data. In the case of the Bank macrophyte dataset the classifications provided for
each sampling period were significant and agreed well with each other, suggesting little
seasonal variation in classification structure (Figure 3.5). For the Aquatic macrophyte dataset
significant cluster groupings were not created for any of the sampling periods or for a dataset of
average values over the 3 sampling periods. Data from each of the three sampling periods for
both the Aquatic and Bank macrophyte datasets were then combined for analysis, this provided
a higher number of samples on which to base the classification and increased the likelihood of
creating significant clusters for the Aquatic macrophyte dataset. While this approach means that
seasonal variation could impact the classification, the analysis of temporal variation in Section
3.4.2 above suggest that this will be minimal. Furthermore, there is no assumption by the
CLUSTER analysis that samples are independent. The classification analyses of both combined
datasets (Bank macrophytes and Aquatic macrophytes) produced cluster groups that were
significant (Figures 3.7 and 3.8) and in the case of the Bank macrophyte dataset agreed well
with the seasonal classifications (Figure 3.5).
57
a)
b)
c)
Figure 3.5 Hierarchical cluster dendrograms of sites for the Bank macrophyte dataset for a)
June 2006, b) September 2006 and c) June 2007. Divisions of the dendrogram coloured in black
are significant at the p<0.05 level. Coloured symbols show membership of significant cluster
groupings in each dendrogram
58
3.4.3.1 The Bank Macrophyte Classification
SIMPROF analysis showed that there was underlying structure in the data at 100 % average
similarity (Figure 3.7). This suggests that the samples most similar to each other are the
seasonal replicates of each site and that differences between sites are greater than the seasonal
variation between sites. However, it does not highlight groupings of sites which are similar to
each other. To detect groupings of sites, the classification was tested at a lower percentage of
average similarity using the physico-chemical data collected. Only divisions of the dendrogram
which were found to be significant by the SIMPROF routine and which did not result in
increasing numbers of end groups containing only one site were tested. Kruskal Wallis tests
showed significant differences between the groups at 52 % average similarity (Figure 3.7).
There were significant differences between these groups based on alkalinity (χ²=15.161,
p=0.010), substrate (χ²=16.219, p=0.006), flow (χ²=20.548, p=0.001), stream order (χ²=66.211,
p=0.000), presence/absence of a bridge (χ²=24.478, p=0.000), wet width (χ²=30.306, p=0.000),
depth (χ²=21.095, p=0.000) and slope (χ²= 24.173, p=0.000) (Table 3.5). Site values for the
physical and physico-chemical characteristics are presented in Table 3.6, grouped according to
the final classification.
Table 3.5 Kruskal Wallis tests results for differences between Bank macrophyte cluster groups
(n=77). Significant values are marked with an asterisk.
χ² df p
TRP mg/L P 2.779 5 0.734
NH4 mg/L N 3.378 5 0.642
DO mg/L O2 7.821 5 0.166
BOD mg/L O2 5.824 5 0.324
TDS g/L 9.795 5 0.081
Conductivity µS/cm 7.635 5 0.178
Alkalinity mg/L CaCO3 15.161 5 0.010*
pH 5.262 5 0.385
Substrate 16.219 5 0.006*
Flow 20.548 5 0.001*
Shading 2.369 5 0.796
Stream Order 66.211 5 0.000*
Bridge 24.478 5 0.000*
Wet width 30.306 5 0.000*
Depth 21.095 5 0.001*
Slope 24.173 5 0.000*
59
Figure 3.7 The Bank macrophyte classification for the Milltown Lake catchment based on combined seasonal datasets. Divisions of the
dendrogram coloured in black are significant at the p<0.05 level. The dotted line shows the percentage similarity at which the cluster groups
were created. Coloured symbols show membership of each cluster group
60
Table 3.6 Physical characteristics and median physico-chemical parameter values (n=3) of sites in the bank macrophyte cluster groups.
Substrate refers to the particle type ranked highest in order of dominance. Flow refers to the dominant flow type recorded. Presence of a
bridge is indicated by 1 and absence by 0
Group Site TRP NH4 DO BOD TDS Cond. Alk. pH Width Depth Substrate Flow Shade Bridge Order Slope
1 TV6 0.102 0.059 9.96 1.95 95.9 148.30 66.00 7.71 1.87 0.11 Cobble Riffle Med 0 2 8.33
2 TV4 0.065 0.199 9.45 2.09 135.7 226.00 74.00 7.02 1.50 0.15 Boulder Glide Med 1 2 50.00
3 TV1 0.029 0.061 5.67 3.48 134.1 222.00 44.00 7.04 1.50 0.20 Boulder Riffle Med 1 1 6.25
TV2 0.074 0.042 8.37 2.29 140.9 146.50 54.00 7.36 0.90 0.10 Boulder Glide Med 0 1 50.00
GO1 0.100 0.127 8.45 2.59 115.7 193.10 62.00 7.70 1.00 0.10 Grav/peb Pool High 0 1 5.56
GO2 0.136 0.033 8.63 1.19 78.0 144.40 48.00 6.88 1.12 0.12 Cobble Riffle Med 0 1 14.29
GO4 0.087 0.031 8.92 1.72 74.9 121.85 39.00 7.63 1.30 0.30 Cobble Riffle Med 1 1 50.00
GO6 0.064 0.027 9.68 3.08 77.3 142.80 70.00 8.00 1.00 0.16 Grav/peb Glide Low 1 1 3.45
TH1 0.077 0.072 8.06 2.36 96.8 161.30 48.00 7.31 1.50 0.15 Cobble Riffle High 1 1 12.50
TH2 0.143 0.034 7.98 3.78 59.7 113.80 50.00 7.58 1.50 0.10 Cobble Glide High 1 1 12.50
TH6 0.143 0.032 8.47 2.69 129.6 195.40 54.00 7.48 1.30 0.13 Cobble Riffle Low 1 2 7.69
MA2 0.058 0.152 8.71 3.38 105.9 131.50 42.00 7.42 1.00 0.50 Grav/peb Glide High 0 1 50.00
MA4 0.018 0.107 8.39 0.40 99.4 136.00 44.00 7.09 0.87 0.17 Cobble Riffle Med 1 1 20.00
CN1 0.084 0.048 9.25 2.88 124.9 157.30 84.00 7.22 0.75 0.10 Cobble Glide High 1 1 14.29
4 GO3 0.093 0.029 9.44 3.35 112.0 194.00 48.00 8.04 0.75 0.16 Cobble Glide Low 0 1 50.00
GO5 0.127 0.036 9.43 3.95 115.6 192.70 68.00 7.62 2.30 0.16 Cobble Riffle High 1 1 7.69
TH3 0.091 0.033 9.51 3.48 100.7 165.70 52.00 7.47 1.80 0.13 Cobble Riffle High 1 1 33.33
TH4 0.048 0.064 11.83 3.65 77.7 137.50 52.00 7.90 1.50 0.15 Cobble Riffle Low 0 1 16.67
DG1 0.047 0.084 8.65 4.13 104.5 109.80 44.00 7.54 1.00 0.20 Cobble Riffle Med 0 1 20.00
DG2 0.010 0.049 8.72 1.89 105.0 115.60 54.00 7.72 2.00 0.15 Grav/peb Glide High 0 1 16.67
5 D1 0.018 0.057 10.66 3.78 101.1 142.40 64.00 7.50 4.00 0.27 Sand Glide Med 1 3 5.56
D2 0.015 0.048 11.50 2.85 88.1 145.00 64.00 8.15 4.00 0.25 Boulder Glide Med 1 3 5.56
6 TV3 0.056 0.045 8.54 1.99 98.5 164.20 76.00 7.82 2.00 0.20 Cobble Glide Med 1 2 20.00
TV5 0.068 0.046 9.98 3.65 145.3 155.90 68.00 7.28 2.00 0.17 Boulder Glide Med 1 2 10.00
TV7 0.047 0.044 10.04 3.95 137.6 229.00 76.00 7.82 2.50 0.20 Bedrock Glide High 1 2 8.33
TH5 0.075 0.046 9.05 3.88 123.6 186.80 54.00 7.32 1.25 0.10 Cobble Glide Low 1 2 14.29
61
Group 1
The first division of the dendrogram showed that the site TV6 was least similar to all the other
sites in the catchment. It separated at only 10.88 % average similarity with the other sites
(Figure 3.7). The macrophyte community at TV6 was different to all other sites due to the
paucity of higher plant species. C. palustris was the only higher plant recorded at the site
(Appendix 1). Although the Kruskal Wallis tests showed significant differences between cluster
groups based on physical and physico-chemical characteristics (Table 3.5), these analyses were
based on only 3 values for Group 1 (Table 3.6).
Group 2
The next group created by the cluster analysis also consisted of only one site, TV4. This cluster
group had the highest median ammonium concentration (Table 3.8). However, as with Group 1,
only 3 values were included in the Kruskal Wallis analysis for differences between physical and
chemical characteristics (Table 3.6). The macrophyte flora at TV4 was characterised by the
presence of T. alopecurum (0.91) (Table 3.7).
Group 3
The third cluster was the largest group created and comprised sites from all the main tributaries
(TV1, TV2, GO1, GO2, GO4, GO6, TH1, TH2, TH6, MA2, MA4 and CN1) (Figure 3.7). All
sites in this group are located on first order streams, except TH6, which is on a second order
stream (Table 3.6). Furthermore, all of the sites in this group are less than 1.5 m in width and
less than 0.5 m in depth. This may suggest a relationship between these sites based on size. This
group is characterised by the presence of V. anagallis aquatica (0.34), J. effusus (0.31), R.
flammula (0.52), A. sylvestris (0.49), E. arvense (0.74) and F. ulmaria (0.30) (Table 3.7).
Group 4
The next significant cluster highlighted by the classification analysis included the sites TH3,
TH4, GO3, GO5, DG1 and DG2 (Figure 3.7). These were mostly first order streams, the
majority of which were dominated by cobble substrates and riffle habitats (Table 3.6). There
were no significant indicator species for this group. Taxa which were notably absent from this
group included P. arundinacea and Fissidens sp.
62
Group 5
This group comprised only two sites, D1 and D2. These were the largest sites in the catchment
and were both located along the main inflow to Milltown Lake. These were the only sites on a
third order stream in the catchment and were the two widest sites in the catchment (Table 3.6).
The dominant habitat type at both sites was glide (Table 3.6). This group was characterised by
the presence of Fissidens sp (0.76) and L. cruciata (0.32) (Table 3.7).
Group 6
This cluster group comprised the sites TV3, TV4, TV5, TV7 and GO5 (Figure 3.7). This
grouping represents the rest of the larger sites in the catchment. All sites in this group are
located on second order streams (Table 3.6). The flow type at each of these sites was glide and
there was a bridge at each site in the group (Table 3.3). This group is also dominated by sites on
the TV tributary. This group was characterised by the presence of P. arundinacea (0.43) (Table
3.7).
Table 3.7 Significant indicator species (p<0.05) with INDVAL statistics (in parentheses) for
each of the cluster groups created for the Bank macrophyte dataset
Group 2 Group 3 Group 5 Group 6
Thal (0.91) Ansy (0.49) Fisp (0.76) Phar (0.43)
Eqar (0.74) Lucr (0.32)
Fiul (0.30)
Juef (0.31)
Rafl (0.52)
Veaa (0.34)
63
Table 3.8 Statistical summaries of physico-chemical values for the Bank macrophyte clusters
TRP
mg/L
NH4
mg/L
DO
mg/L
BOD
mg/L
TDS
g/L
Cond.
µS/cm
Alk.
mg/L
pH
Group 1 Mean 0.105 0.069 9.47 2.21 109.3 180.23 66.00 7.76
Std. Err. 0.052 0.017 0.74 0.45 17.6 32.89 3.46 0.24
Min. 0.017 0.047 8.02 1.59 87.8 146.40 60.00 7.40
Max. 0.197 0.101 10.44 3.08 144.3 246.00 72.00 8.20
N 3 3 3 3 3 3 3 3
Group 2 Mean 0.063 0.238 9.08 2.09 129.3 212.87 74.67 7.147
Std. Err. 0.023 0.127 0.50 0.75 19.5 24.9550 7.51 0.19
Min. 0.021 0.040 8.10 0.80 92.8 164.60 62.00 6.90
Max. 0.102 0.475 9.70 3.38 159.5 248.00 88.00 7.50
N 3 3 3 3 3 3 3 3
Group 3 Mean 0.097 0.078 8.57 2.85 102.0 161.80 54.46 7.43
Std. Err. 0.019 0.014 0.32 0.37 4.7 6.85 2.74 0.09
Min. 0.001 0.001 4.45 0.01 53.5 99.50 18.00 6.80
Max. 0.465 0.370 15.85 10.35 143.1 235.00 88.00 8.90
N 35 35 35 35 35 35 35 35
Group 4 Mean 0.110 0.093 9.45 3.19 95.9 157.71 49.67 7.65
Std. Err. 0.031 0.030 0.42 0.33 4.8 10.07 3.34 0.13
Min. 0.009 0.015 6.80 0.70 66.5 105.20 22.00 6.90
Max. 0.452 0.524 14.00 6.57 123.2 226.00 78.00 8.80
N 18 18 18 18 18 18 18 18
Group 5 Mean 0.084 0.057 10.16 3.04 102.8 156.75 63.33 7.64
Std. Err. 0.067 0.013 0.67 0.47 7.9 16.12 2.35 0.26
Min. 0.000 0.020 8.01 1.49 83.0 135.40 54.00 6.80
Max. 0.415 0.099 11.52 4.78 128.3 237.00 70.00 8.30
N 6 6 6 6 6 6 6 6
Group 6 Mean 0.107 0.121 9.26 3.21 126.0 184.36 64.33 7.57
Std. Err. 0.031 0.062 0.33 0.34 8.1 12.40 4.40 0.15
Min. 0.011 0.021 7.52 1.29 87.6 126.20 28.00 7.00
Max. 0.304 0.787 11.16 5.37 171.4 260.00 80.00 8.60
N 12 12 12 12 12 12 12 12
64
3.4.3.2 Aquatic Macrophyte Classification
SIMPROF analysis showed that there was underlying structure in the data at 73.49 % average
similarity (Figure 3.8). As with the Bank macrophyte classification this produced a large
number of end groups with only one site and is a result of the relationship between the seasonal
replicates. End groups at a lower percentage average similarity were tested using the physico-
chemical data collected. Kruskal Wallis tests showed significant differences between the groups
at 37.9 % average similarity (Figure 3.8). There were significant differences between these
groups based TDS (χ²=12.242, p=0.045), alkalinity (χ²=19.877, p=0.001), substrate (χ²=22.466,
p=0.000), stream order (χ²=18.729, p=0.001), presence/absence of a bridge (χ²= 14.486,
p=0.006) and wet width (χ²=18.490, p=0.001) (Table 3.9). Site values for the physical and
physico-chemical characteristics are presented in Table 3.10, grouped according to the final
classification.
Table 3.9 Kruskal Wallis tests results for differences between Aquatic macrophyte cluster
groups (n=77). Significant values are marked with an asterisk.
χ² df p
TRP mg/L P 1.025 4 0.906
NH4 mg/L N 2.449 4 0.654
DO mg/L O2 9.285 4 0.054
BOD mg/L O2 1.746 4 0.782
TDS g/L 12.242 4 0.016*
Conductivity µS/cm 7.859 4 0.097
Alkalinity mg/L CaCO3 19.877 4 0.001*
pH 3.576 4 0.466
Substrate 22.466 4 0.000*
Flow 7.404 4 0.116
Shading 7.786 4 0.100
Stream Order 18.729 4 0.001*
Bridge 14.486 4 0.006*
Wet width 18.490 4 0.001*
Depth 3.397 4 0.494
Slope 6.345 4 0.175
65
Figure 3.8 The Aquatic macrophyte classification for the Milltown Lake catchment based on combined seasonal datasets. Divisions of the
dendrogram coloured in black are significant at the p<0.05 level. The dotted line shows the percentage similarity at which the cluster groups
were created. Coloured symbols show membership of each cluster group
66
Table 3.10 Physical characteristics and mean physico-chemical parameter values (n=3) of sites in Aquatic macrophyte cluster groups.
Substrate refers to the particle type ranked highest in order of dominance. Flow refers to the dominant flow type recorded. Presence of a
bridge is indicated by 1 and absence by 0
Group Site TRP NH4 DO BOD TDS Cond. Alk. pH Substrate Flow Shade Order Bridge Width Depth Slope
1 TV1 0.029 0.061 5.67 3.48 134.1 222.00 44.00 7.04 Boulder Riffle Med 1 1 1.50 0.20 6.25
2 TH1 0.077 0.072 8.06 2.36 96.8 161.30 48.00 7.31 Cobble Riffle High 1 1 1.50 0.15 12.50
MA2 0.058 0.152 8.71 3.38 105.9 131.50 42.00 7.42 Grav/peb Glide High 1 1 1.00 0.50 50.00
3 D1 0.018 0.057 10.66 3.78 101.1 142.40 64.00 7.50 Sand Glide Med 3 1 4.00 0.27 5.56
TV2 0.074 0.042 8.37 2.29 140.9 146.50 54.00 7.36 Boulder Glide Med 1 0 0.90 0.10 50.00
GO1 0.100 0.127 8.45 2.59 115.7 193.10 62.00 7.70 Grav/peb Pool High 1 0 1.00 0.10 5.56
TH4 0.048 0.064 11.83 3.65 77.7 137.50 52.00 7.90 Cobble Riffle Low 2 1 1.50 0.15 16.67
TH6 0.143 0.032 8.47 2.69 129.6 195.40 54.00 7.48 Cobble Riffle Low 1 0 1.30 0.13 7.69
DG2 0.010 0.049 8.72 1.89 105.0 115.60 54.00 7.72 Grav/peb Glide High 1 1 2.00 0.15 16.67
4 D2 0.015 0.048 11.50 2.85 88.1 145.00 64.00 8.15 Boulder Glide Med 3 1 4.00 0.25 5.56
TV3 0.056 0.045 8.54 1.99 98.5 164.20 76.00 7.82 Cobble Glide Med 2 1 2.00 0.20 20.00
TV4 0.065 0.199 9.45 2.09 135.7 226.00 74.00 7.02 Boulder Glide Med 2 1 1.50 0.15 50.00
TV5 0.068 0.046 9.98 3.65 145.3 155.90 68.00 7.28 Boulder Glide Med 2 1 2.00 0.17 10.00
TV6 0.102 0.059 9.96 1.95 95.9 148.30 66.00 7.71 Cobble Riffle Med 2 0 1.87 0.11 8.33
TV7 0.047 0.044 10.04 3.95 137.6 229.00 76.00 7.82 Bedrock Glide High 2 1 2.50 0.20 8.33
GO5 0.127 0.036 9.43 3.95 115.6 192.70 68.00 7.62 Cobble Riffle High 1 1 2.30 0.16 7.69
5 GO2 0.136 0.033 8.63 1.19 78.0 144.40 48.00 6.88 Cobble Riffle Med 1 0 1.12 0.12 14.29
GO3 0.093 0.029 9.44 3.35 112.0 194.00 48.00 8.04 Cobble Glide Low 1 0 0.75 0.16 50.00
GO4 0.087 0.031 8.92 1.72 74.9 121.85 39.00 7.63 Cobble Riffle Med 1 1 1.30 0.30 50.00
GO6 0.064 0.027 9.68 3.08 77.3 142.80 70.00 8.00 Grav/peb Glide Low 1 1 1.00 0.16 3.45
TH2 0.143 0.034 7.98 3.78 59.7 113.80 50.00 7.58 Cobble Glide High 1 1 1.50 0.10 12.50
TH3 0.091 0.033 9.51 3.48 100.7 165.70 52.00 7.47 Cobble Riffle High 1 0 1.80 0.13 33.33
TH5 0.075 0.046 9.05 3.88 123.6 186.80 54.00 7.32 Cobble Glide Low 2 1 1.25 0.10 14.29
MA4 0.018 0.107 8.39 0.40 99.4 136.00 44.00 7.09 Cobble Riffle Med 1 0 0.87 0.17 20.00
DG1 0.047 0.084 8.65 4.13 104.5 109.80 44.00 7.54 Cobble Riffle Med 1 0 1.00 0.20 20.00
CN1 0.084 0.048 9.25 2.88 124.9 157.30 84.00 7.22 Cobble Glide High 3 1 0.75 0.10 14.29
67
Group 1
The site TV1 was divided from the other groups at 29.92 % average similarity (Figure 3.8).
Although the Kruskal Wallis tests showed significant differences between cluster groups based
on physical and physico-chemical characteristics (Table 3.9), these analyses were based on only
3 values for Group 1 (Table 3.10). The macrophyte community at this site was characterised by
the presence of E. canadensis (1.00) and T. latifolia (1.00), neither of which were recorded
anywhere else in the catchment and also the presence of L. minor (0.54), C. obtusangula (0.46),
G. maxima (046) and L. riparium (0.58) (Table 3.12).
Group 2
This group contained only two sites (MA2 and TH1), both of which were characterised by the
presence of R. nasturtium aquaticum (0.43) and S. emersum (0.39) (Table 3.12). Both sites had
an impoverished flora with only three aquatic species being recorded at MA2 and only four
being recorded at TH1 (Appendix 1). As with Group 1, only 3 values were included in the
Kruskal Wallis analyis for differences between physical and chemical characteristics (Table
3.10).
Group 3
The next significant cluster group, created at 31.58 % average similarity comprised the sites
D1, TV2, GO1, TH4, TH6 and DG2 (Figure 3.8). There were no significant indicator species
for the sites in this cluster group. The species F. antipyretica, L. riparium and R. nasturtium
aquaticum were recorded at each of the sites in this cluster group (Appendix 1). This group
showed greater homogeneity than either group 4 or 5 with regard to substrate, habitat type,
stream order and presence/absence of bridge (Table 3.10).
Group 4
This group contained the sites TV3, TV4, TV5, TV7, GO5 and D2 and was characterised by the
presence of the species O. crocata (0.92) and P. endiviifolia (0.39) (Table 3.12). The sites in
this cluster group represented a range of physical characteristics (Table 3.10), however most
sites were on second order streams and most had a bridge within their length. Sites of this group
68
also had lower median conductivity and alkalinity concentrations (Table 3.11) and were wider
than sites in group 5 (Table 3.10).
Group 5
This group contained the sites GO2, GO3, GO4, GO6, TH2, TH3, TH5, CN1, MA4 and TV6
(Figure 3.7). The majority of sites in this cluster group were dominated by cobble substrate
(Table 3.10). This group is also dominated by sites on the GO tributary. The sites in this cluster
were characterised by the presence of F. antipyretica (0.37) and B. rivulare (0.42) (Table 3.12).
Table 3.11 Statistical summaries of physico-chemical values for Aquatic macrophyte clusters
TRP
mg/L
NH4
mg/L
DO
mg/L
BOD
mg/L
TDS
g/L
Cond.
µS/cm
Alk.
mg/L
pH
Group 1 Mean 0.042 0.058 5.75 4.71 136.3 189.13 47.33 7.05
Std. Err. 0.018 0.015 0.77 1.60 2.4 33.87 8.82 0.12
Min. 0.018 0.030 4.45 2.78 133.7 121.40 34.00 6.90
Max. 0.079 0.083 7.13 7.88 141.1 224.00 64.00 7.30
N 3 3 3 3 3 3 3 3
Group 2 Mean 0.123 0.105 8.54 2.71 92.3 147.72 45.67 7.37
Std. Err. 0.071 0.035 0.56 0.48 7.8 11.37 4.77 0.16
Min. 0.001 0.002 6.88 1.19 58.3 111.10 26.00 6.80
Max. 0.465 0.231 10.45 4.16 108.6 178.00 62.00 7.80
N 6 6 6 6 6 6 6 6
Group 3 Mean 0.109 0.106 9.16 3.26 109.2 163.36 55.67 7.60
Std. Err. 0.034 0.032 0.44 0.52 5.7 9.15 3.07 0.14
Min. 0.005 0.015 6.47 0.99 68.4 114.10 22.00 6.80
Max. 0.452 0.524 14.00 10.35 143.1 235.00 88.00 8.90
N 18 18 18 18 18 18 18 18
Group 4 Mean 0.102 0.064 9.22 2.67 95.9 157.55 53.44 7.56
Std. Err. 0.018 0.011 0.30 0.30 4.4 7.32 2.99 0.09
Min. 0.009 0.001 6.21 0.01 53.5 99.50 18.00 6.80
Max. 0.393 0.258 15.85 8.26 144.3 246.00 84.00 8.80
N 32 32 32 32 32 32 32 32
Group 5 Mean 0.088 0.131 9.39 2.90 119.1 188.78 69.22 7.50
Std. Err. 0.024 0.047 0.30 0.28 7.2 10.53 2.34 0.12
Min. 0.000 0.020 7.52 0.80 76.7 126.90 48.00 6.90
Max. 0.304 0.787 11.52 5.37 171.4 260.00 88.00 8.30
N 18 18 18 18 18 18 18 18
69
Table 3.12 Significant indicator species (p<0.05) with INDVAL statistics (in parentheses) for
each of the cluster groups created for the Aquatic macrophyte dataset
Group 1 Group 2 Group 4 Group 5
Lemi (0.54) Spem (0.39) Oecr (0.92) Foan (0.37)
Caob (0.46) Rona (0.43 Peen(0.39) Brri (0.42)
Amri (0.58)
Elca(1)
Glma (0.46)
Tyla (1))
3.4.4 Relationship between the Macrophyte Community and Site Physico-
chemical Characteristics
3.4.4.1 Bank Macrophyte Taxa
The DCA carried out on the Bank Macrophyte dataset and the dataset of annual median
physico-chemical parameters showed a gradient length of 3.739 for the first axis (Table 3.16),
indicating high beta diversity in the data and suggesting that they were best suited to unimodal
methods of ordination. However, the relationship between the unimodal model (CCA) created
to explain the variance in the Bank Macrophyte dataset was not found to be significant by the
Monte Carlo permutation test (F=0.953, p=0.484).
Table 3.16 Summary statistics of DCA for the Bank macrophyte dataset
Axes 1 2 3 4 Total inertia
Eigenvalues : 0.387 0.180 0.099 0.053 1.820
Lengths of gradient : 3.739 1.962 1.358 1.390
Species-env. correlations : 0.351 0.413 0.740 0.539
Cumulative percentage variance
of species data : 21.2 31.1 36.6 39.5
of species-env. relation : 5.9 12.5 0.0 0.0
Sum of all eigenvalues 1.820
Sum of all canonical eigenvalues 0.431
70
3.4.4.2 Aquatic Macrophyte Taxa
The DCA carried out on the Aquatic Macrophyte dataset showed a gradient length of 2.919 for
the first axis (Table 3.17), suggesting that they were best suited to linear methods of ordination
(RDA). The relationship between the model created to explain the variance in the Aquatic
Macrophyte dataset suggested by the RDA analysis was found to be significant by the Monte
Carlo permutation test (F=1.669, p=0.008). The measured environmental variables described
35.8 % of the variation in the Aquatic Macrophyte dataset (Table 3.18). The results indicated
that axis 1 and 2 explained 24.2 % of the fitted biological data. The first axis of the RDA
described 15.1 % of the overall variation. The ordination diagram shows that this axis was
associated with TRP, NH4 and TDS (Figure 3.10). The second axis of the RDA described a
further 9.1 % of the variation in the Aquatic Macrophyte dataset. This axis was positively
associated with DO, pH and BOD.
Table 3.17 Summary statistics of DCA for the combined Aquatic macrophyte dataset
Axes 1 2 3 4 Total inertia
Eigenvalues : 0.490 0.231 0.130 0.068 1.907
Lengths of gradient : 2.919 2.617 2.243 1.949
Species-env. correlations : 0.782 0.408 0.657 0.765
Cumulative percentage variance
of species data : 25.7 37.8 44.7 48.2
of species-env. relation : 34.0 39.1 0.0 0.0
Sum of all eigenvalues 1.907
Sum of all canonical eigenvalues 0.811
Table 3.18 Summary statistics of RDA for the combined Aquatic macrophyte dataset
Axes 1 2 3 4 Total variance
Eigenvalues : 0.151 0.091 0.060 0.030 1.000
Species-env. correlations : 0.893 0.783 0.798 0.465
Cumulative percentage variance
of species data : 15.1 24.2 30.2 33.2
of species-env. relation : 42.3 67.8 84.6 93.0
Sum of all eigenvalues 1.000
Sum of all canonical eigenvalues 0.358
71
Figure 3.10 RDA ordination diagram of the combined Aquatic macrophyte dataset and physico-
chemical parameters
72
3.5 Discussion
3.5.1 The Macrophyte Flora of the Milltown Lake Catchment
Many of the species recorded in the Milltown Lake catchment (S. emersum, L. minor, C.
obtusangula, G. maxima, E. canadensis, R. nasturtium aquaticum, A. plantago-aquatica, T.
latifolia) are associated with slow to moderate flows, mesotrophic to eutrophic waters and fine
to medium substrate particle sizes (Haslam et al. 1975; Haslam 1978; Preston & Croft 1996).
The macrophyte flora of the Milltown Lake catchment includes a number of species
characteristic of basic geology e.g. L. riparium, T. alopecurum (Watson 1981), C. obtusangula,
E. canadensis, R. nasturtium aquaticum, O. crocata (Preston & Croft 1996) and circum neutral
waters e.g. B. rivulare, C. polyanthos, Platyhypnium riparioides (Hedw.) Dixon, F. antipyrteica
(Watson 1981; Holmes 2009), G. maxima, R. flammula (although this species can also be
present on more acid substrates) (Preston & Croft 1996). These findings are in accordance with
the information gathered in relation to the geology of the area in the desktop study (Chapter 2),
the site characteristics recorded (Chapter 2) and with the physico-chemical survey results
(Appendix 2).
The species recorded in this study overlap with those recorded in the Fane catchment by Heuff
(1984b) and may provide further evidence for the Glycerio-Sparganion x Apion nodiflori
association suggested by that author for the smaller streams of the Fane. There is also some
overlap with the Group B River Community Type V classification which forms part of the
classification system for British rivers (Holmes et al. 1998). Although the current study
provides data on macrophyte presence and abundance at a high spatial intensity, the number of
species per site recorded in the Milltown Lake catchment agrees with those obtained in other
Irish studies (Caffrey 1985; Kelly-Quinn et al. 2005; Matson 2006). Other studies in Britain
(Holmes & Whitton 1977) and continental Europe (Triest 2006; Grinberga 2010) have also
recorded similar numbers of species per site.
The most frequently recorded bryophyte in this survey, F. antipyretica is regarded as a
generalist (Watson 1981; Haslam 1978; Scarlett & O‘Hare 2006), having been recorded along a
broad ecological range. Another bryophyte commonly recorded in this study, P. endiviifolia has
73
previously been recorded at a variety of altitudes and along a gradient of pollution at sites
throughout Ireland (Matson 2006). Indeed, it was the most ubiquitous taxon recorded in that
study. This suggests that its abundance here is not of local importance, but that it is common
throughout Ireland. P. riparioides has also been recorded at a wide range of sites (Scarlett &
O‘Hare 2006), being able to tolerate both acid and calcareous conditions (Watson 1981). The
presence of the mosses L. riparium and B. rivulare, however are associated with nutrient
enrichment (Dawson et al. 1999; Scarlett & O‘Hare 2006) and are not found along as wide a
gradient as either F. antipyretica, P. endiviifolia or P. riparioides.
Dominance of the Bryophyta has been reported at upland, oligotrophic sites (Grasmück et al.
1995; Holmes et al. 1998; Scarlett & O‘Hare 2006), but the sites of the Milltown Lake
catchment do not fit this general type. These sites flow through agricultural land and are subject
to the eutrophication and organic pollution pressures typical of such rivers (as suggested by the
intermittently high nutrient concentrations recorded (Appendix 2) and the presence of L.
riparium and filamentous algae at some of the sites). However, these sites drain the small hills
characteristic of the drumlin region with steep slopes and so may represent an intermediate state
between those upland, oligotrophic types and large, deep, slow-flowing, lowland sites.
Baattrup-Pedersen et al. (2006), in a study to highlight the relationships between macrophytes
and physico-chemical characteristics in a network of reference condition sites throughout
Europe found that the macrophyte community did not show good agreement with the WFD
defined typologies. Comparison of classifications based on the WFD typology and the
macrophyte communities showed that an important stream community type was overlooked,
that which is intermediate between small upland streams and medium sized lowland streams.
This type was compared to the Group B community identified by Holmes et al. (1998) and may
include the streams of the Milltown Lake catchment. The stream type identified by Baattrup-
Pedersen et al. (2006) was characterised by small size, relatively steep gradients, mixed
geologies and intermediate altitudes. This is in good agreement with the many of the sites of the
Milltown Lake catchment and of the wider drumlin region (Zhou et al. 2000; Douglas et al.
2007). These sites were also characterised by a mixture of vascular and non-vascular plant
species, as were those of the current study.
74
Classifications based on macrophytes at reference sites showed good agreement with the WFD
typology proposed for Ireland (Kelly-Quinn et al. 2005). However, the authors provide the
caveat that the sites included in the classification do not necessarily represent all types in
Ireland due to a lack of data on reference conditions (Kelly-Quinn et al. 2005; Kelly-Quinn et
al. 2009). In fact some types were only represented by one site in the dataset. Reference
conditions for sites in the drumlin landscape may be particularly difficult to assign due to the
impacts on water quality of the intensive agricultural practices in the area as a whole (Gibson et
al. 1980; Gibson et al. 2003). The consequences for use of type specific ecological quality tools
on macrophyte communities in streams which are not adequately described by the typology
may include misclassification. This may occur due to the inability of certain typologies to
differentiate these sites.
3.5.2 Temporal Variation in Taxon Abundances and Richness
The median macrophyte abundance was greater in the September 2006 sampling period than for
either June 2006 or June 2007. This was true for both the Bank macrophyte dataset and the
Aquatic macrophyte dataset. However, statistical analysis of the distribution of taxon
abundance over the catchment for both datasets showed that this difference was not significant.
Also, differences in macrophyte abundance were not significant between years. Increases in
macrophyte abundance over the growing period of June to September are expected for many
species (Cotton et al. 2006; Hrivnák et al. 2009), and cause disturbance where channels become
choked and navigation impeded by a ‗nuisance‘ species (Kaenel & Uehlinger 1999; Caffrey et
al. 2006; Vereecken et al. 2006). Increases in abundance throughout the growing season are
also viewed as one of the greatest sources of variability when comparing vegetation surveys
(Brabec & Szoszkiewicz 2006; Staniszewski et al. 2006). Some survey methodologies state that
surveys for comparison should be carried out at the same time of the year (Holmes et al. 1999;
NSSHARE 2008). As increases in macrophyte abundance were not significant in the Milltown
lake catchment, growth of macrophytes may be limited by some factor. Sand-Jensen (1998)
suggests that macrophytes in Europe tend not to be nutrient limited but that physical factors
play a large part in limiting their spread. As abundant growth of macrophytes is associated with
siltier substrates (Dodkins et al. 2005a; Grinberga 2010), the dominance of cobble and
75
gravel/pebble at many of the sites surveyed in the Milltown Lake catchment may have limited
the growth of species that often reach high abundances in rivers e.g. S. emersum, C.
obtusangula, E. canadensis, or discouraged the establishment of other species associated with
siltier substrates and high seasonal abundances e.g. Ranunculus spp. (Butcher 1933; Haslam et
al. 1975).
As with abundance, statistical analysis of taxon richness showed no significant difference in the
median number of taxa for either the Bank macrophyte dataset or the Aquatic macrophyte
dataset, either between seasons or between years. The examples of new records at a number of
sites in the Milltown Lake catchment for September 2006 included fast growing algal species
and L. minor, which could have floated downstream. This low temporal variation in abundance
suggests that differences between survey results at particular sites throughout the growing
season will not be significant. Implications of this low variation include the potential to
compare results from repeat surveys in following years irrespective of when in the growing
season they were carried out.
3.5.3 Classification of sites on the Milltown Lake Catchment.
Although classification of the bank macrophyte dataset resulted in the creation of 6 groups, two
of these contained only one site (TV4 and TV6). Another group (Group 4) did not have any
significant indicator species. The remaining groups seem to be comprised of sites which were
broadly divided based on stream size. Group 3 was comprised of the smaller sites. This group
had a number of indicator species, the strongest of which was E. arvense. Groups 5 and 6
seemed to share some similar physical characteristics (e.g. glide as the dominant flow type)
with the most obvious difference being the difference in size between sites of the two groups
(1.25 – 2.5 m in Group 5 and 4 m in Group 6). This seemed to be an important distinction
however, as Group 5 was characterised by the presence of the bryophytes Fissidens sp. and L.
cruciata and Group 6 was characterised by the higher plant P. arundinacea.
The only physico-chemical parameter of the water column that was significantly different
between the cluster groups was alkalinity. As alkalinity is related to geology it may underlie a
76
relationship between the bank species and geology. The majority of differences between the
bank cluster groups were related to the physical characteristics of the sites with smaller
upstream sites being indicated by species such as E. arvense and larger downstream sites being
indicated by taxa such as Fissidens sp., L. cruciata and P. arundinacea. This relationship
between P. arundinacea and larger, lower gradient streams is in agreement with the findings of
Matson (2006), who did not record P. arundinacea at any of the reference headwater sites
surveyed in that study. It also shows agreement with the study of Grasmück et al. (1995) and
Battrup-Pedersen et al. (2006) who found taller emergent macrophytes at downstream sites. The
relationship between the bank macrophyte taxa and some of the hydromorphological
characteristics of the site may give weight to the suggestion of Kelly-Quinn et al. (2005) that
bank macrophyte taxa might be important for the assessment of hydromorphology.
It might also be the case that riparian vegetation is better related to physico-chemical conditions
of higher flow events, during which such species would be under the direct influence of the
water column and during which deposits of substrate might occur (Vervuren et al. 2003). If
bank macrophytes are structured by such events then the monthly sampling carried out in this
study which may represent low flow conditions during the sampling periods (Demars & Harper
2005), may not be able to explain their variation. As it was not possible to relate the
characteristics of the water quality parameters during high flow events to the bank macrophyte
dataset, it remains uncertain as to which might structure the community more, the
hydromorphological elements or the high flow water quality parameters.
There was very little overlap between the cluster groups created for the Bank macrophyte
dataset and the Aquatic macrophyte dataset. Even the cluster groups with only one site showed
no overlap between the two datasets. There was some overlap between Group 6 in the bank
macrophyte dataset and Group 4 in the Aquatic macrophyte dataset. The similarity between
these groups lies in the inclusion of TV3, TV5 and TV7 from the TV tributary. These sites were
of a similar size, were all on a second order stream, had a bridge within the survey length and
the dominant habitat type was glide. The indicator species for this group was P. arundinacea in
the Bank macrophyte dataset and O. crocata in the Aquatic macrophyte dataset. The range of
O. crocata is from acid to calcareous habitats and it is often found in stable, upland rivers
77
(Holmes 2009). This relationship between O. crocata and upland sites may further suggest that
the sites of the Milltown Lake catchment represent an intermediate state between upland and
lowland sites.
The classification analyses showed that some of the physical characteristics of the sites were
significant between many of the cluster groups. This held true for both the Bank and Aquatic
macrophyte datasets and has been reported by other authors (e.g. Demars & Harper 1998;
Daniel et al. 2006; Grinberga 2010). Most sites in the Milltown Lake catchment shared many
physical similarities e.g. bed stability, water clarity allowing them to be compared like for like
(Holmes et al. 1999). However factors which discriminated between cluster groups included
physical characteristics such as width, depth, shading, stream order, substrate and
presence/absence of a bridge. These characteristics may be viewed as a function of stream size
as rivers are understood to increase in depth, width and stream order from source to sea, with
substrate particle size generally decreasing (Vannote et al. 1980). Increasing stream size has
been associated with increased habitat heterogeneity and resulting increases in aquatic species
diversity (Beisel et al. 2000; Baattrup-Pedersen et al. 2006). Increased diversity will affect
community composition and classifications.
The impact of bridges on macrophyte surveys has been investigated in Ireland by Matson
(2006). The presence of a bridge at sampling sites in that study did not result in significantly
higher taxon richness. That finding is in agreement with the current study, while bridges were
not associated with increased species richness, they were associated with increased macrophyte
abundance (Appendix 2). Matson (2006) further found that bridges were not significantly
associated with any particular species or physical parameter and it was concluded that sampling
at a bridge had little or no effect on the study. Bridges were included at a number of sampling
sites in this study due to the ease of access and sampling was undertaken up or downstream of
the bridge. It is possible that the importance of the presence/absence of a bridge to macrophyte
abundance in this study might be related to the increased habitat heterogeneity provided by
bridges, including changes in light, flow and sediment accumulation (Becker 1995). Bridges
can increase light penetration to the stream bed in small streams shaded by riparian vegetation
as they create gaps in the canopy (although directly underneath there may be decreased light
78
penetration) and changes in flow around bridges can result in the deposition of finer substrate
particles in which plants can establish.
3.5.4 Relationship between macrophyte community and site physico-chemical
characteristics
Ordination analyses carried out on both the bank and aquatic macrophyte datasets found that the
physico-chemical parameters of the water column could only explain significant variation in the
aquatic macrophyte group. Models based on the bank macrophyte dataset were not found to be
significant. This suggests that the variation in the bank macrophyte community in the Milltown
Lake catchment is not constrained by the physico-chemical character of the streams. This may
be attributed to the fact that these are parameters of the water column and it is the aquatic taxa
that are more directly under their influence. Bank species may be included in the analysis of
data from the island of Ireland for the future development of CBAS. Although the tool was
developed based on the relationships between the aquatic taxa and the selected parameters of
water quality in that study, it is recommended that bank species continue to be recorded
(NSSHARE 2008). However, the lack of relationship with the phsyico-chemical parameters of
the water column in the current study suggest the extra resources involved in the recording,
identifying and analysing of such data would not be warranted for water quality assessments in
the Milltown Lake catchment.
The ordination analysis showed that the parameters which explained the most variation in the
macrophyte dataset were TRP, NH4 and TDS. P has been shown to be the major nutrient
structuring macrophyte communities in Britain (Dawson & Szoszkiewicz 1999) and Europe
(Grasmück et al. 1995; Kuhar et al. 2007; Oťaheľová et al. 2007). The importance of P in
structuring macrophyte communities is due to the common situation of P limitation in
freshwaters. Assessment of macrophyte communities has shown that species presence/absence
and abundance will all change along a gradient of P (e.g. Caffrey 1985; Holmes et al. 1999;
Schaumburg et al. 2004; Free et al. 2006; Haury et al. 2006). The range of TRP recorded for the
Milltown Lake catchment is wide enough to structure the macrophyte vegetation as it varies
from below the LOD to 0.68 mg/L P. This is greatly in excess of the ecological quality standard
79
(EQS) of 0.015 mg/L P for high status rivers (S.I. 272/2009) (Minister for the Environment,
Heritage and Local Government 2009). This wide range of TRP concentrations shows that the
catchment has been subject to gross nutrient pollution, but the influence of this variable on the
community is small (part of 15.1 %) and suggests there are other variables exerting a greater
influence on the community.
The response of macrophyte communities to dissolved nitrogen (as nitrate or ammonium) has
also been highlighted in a number of studies (Thiebaut et al. 2002; Dodkins et al. 2005a; Feijoó
& Lombardo 2007; Demars & Edwards 2009). The importance of ammonium is in agreement
with the findings of Dodkins et al. (2005a), who found stronger relationships between
macrophyte communities in Ireland and ammonium, rather than P. Dodkins et al. (2005a)
suggest that this may be a factor of the agricultural landscape in Ireland. N has been identified
as the limiting nutrient at a number of sites in rivers in the west of Ireland where P limitation
had been assumed to be the issue (Walsh 2005). The NH4 concentrations recorded in this study
for the Milltown Lake catchment range from below the 0.04 mg/L N value set as the EQS for
high status rivers (S.I. 272/2009) (Minister for the Environment, Heritage and Local
Government 2009), to over an order of magnitude higher than the 0.065 mg/L N value set as the
EQS for good status sites (there are no EQSs currently set for other status classes). The EPA
monitoring site recorded an NH4 concentration of 3.81 mg/L N (Table 2.2). As with the case for
TRP, the influence of this variable on the community is small (part of 15.1 %) and suggests
there are other variables exerting a greater influence on the community.
TDS (or its correlate conductivity) have also been highlighted as explaining variation in
macrophyte communities, generally in small scale studies. Demars & Edwards (2009), working
on sites in England and Scotland and Feijoo & Lombardo (2007), working in the Argentinian
Pampas noted significant variation in the macrophyte communities surveyed explained by
conductivity. The ability of conductivity to explain variation in macrophyte communities has
been attributed to its relationship with enrichment by the major ions P and N (Ecke 2009) and
in fact, could not be separated from such pressures by Demars & Edwards (2009) when trying
to rank and separate the individual effects of local environmental conditions on species
distribution. Activities in the Milltown Lake catchment which could mobilise major ions and
80
impact conductivity include felling of forestry, field drainage, cattle access to water courses and
run off from fields.
The classification analysis and the ordination analysis highlighted different elements of the
physico-chemistry as explaining important variation in the aquatic macrophyte community.
This may be due to the use of a combined seasonal dataset of physico-chemical parameters for
the classification analysis and the use of the dataset of annual median values for the ordination.
The longer term chemistry dataset may indicate the ability of macrophytes to integrate longer
term water quality trends. The significance of the physico-chemical parameters of the water was
much greater when the longer term dataset was used. Egertson et al. (2004) and Sondergaard et
al. (2007) suggest that the time lag for incorporation of environmental conditions by
macrophyte communities in lakes may be up to two years. The length of time required for the
macrophyte community to respond may vary with different conditions e.g. recovery of the
water body to a particular threshold (Monteith et al. 2005). Where pressures persist, they will
discourage the growth of species less tolerant of pollution. This will also take place over longer
periods of time than e.g. phytoplankton and macroinvertebrates (Asaeda et al. 2001; Van De
Meutter et al. 2006) since plant colonisation processes are also related to spatial isolation
(Demars & Harper 2005; Demars & Thiébaut 2008). This suggests that connectivity and scale
are important since the immediate, chance colonisation of a pollution tolerant macrophyte
species after a pollution event is unlikely (Demars & Harper 1998). This response to
environmental conditions over a longer period of time may explain the lack of agreement
between the macrophyte community and the measured physico-chemical parameters, both in
this study and others (Baattrup-Pedersen et al. 2006). While the combined dataset of phyico-
chemical parameters may represent low flow conditions during the sampling periods (Demars
& Harper 2005), the observed macrophyte community may be representing responses to
conditions from previous years (Egertson et al. 2004; Sondergaard et al. 2007). This suggests
that macrophytes might best be used in operational monitoring programs where data on baseline
water quality is desired rather than in investigative monitoring where tools with rapid responses
to impacts might be required.
81
Ordination is a popular method of relating physical or chemical characteristics of sites to
biological communities and is very prevalent in the literature of the last decade. The types of
ordination available for use will depend on the data being analysed and examples of their use
are aplenty (e.g. Ruse 1996; Heegaard et al. 2001; Caffrey et al. 2006; Kuhar et al. 2007;
Skoulikidis et al. 2009). There are, however discrepancies in how the data is presented by
differing authors. The output from the Canoco program can be misleading as the amount of
variance explained by both the species and species-environment relation are detailed giving the
user the impression that this is the amount of variation in the species data that is being
explained by the measured physico-chemical parameters. This is not the case as these
percentages refer to the relationship between the species and the axes created by the analysis
(ter Braak & Šmilauer 2002). These percentages are frequently reported in the literature
(Paszkowski & Tonn 2000; Heegaard et al. 2001; Schaumburg et al. 2004; Caffrey et al. 2006;
Paavola et al. 2006; Oťaheľová et al. 2007) and tend to be higher than the amount of variance
explained by the measured physico-chemical parameters (sum of all canonical
eigenvalues/100). In some publications eigenvalues and percentage variance are not reported
(Iliopoulou-Georgudaki et al. 2003) even though eigenvalues can be used to assess the
significance of the analysis (Jackson 1993). In some studies only the eigenvalues are reported
(Riis et al. 2000).
These discrepancies in presentation of results make comparison difficult. Making comparisons
with the percentage variance reported can be misleading. The percentage variance in the
species-environment data explained by the first axis of the RDA of the Aquatic macrophyte
dataset was 44.8 %. However, the amount of the total variation in the macrophyte data that was
actually explained by that axis was 6 %. Like must be compared with like to provide any
meaningful interpretation. In general the percentage variance explained by the species-
environment data will be the higher figure, with the percentage of the total variance explained
by the physico-chemical parameters being much lower. Values around 25 % are common
(Matson 2006; McElarney & Rippey 2009, Kelly-Quinn, pers. comm.). However, greater
variation in macroinvertebrate communities (30.6 % and 32.4 % respectively) has been
explained using these methods (Murphy & Davy-Bowker 2005; Feld & Hering 2007). Values
near 100% are not generally expected when using such methods of analysis. This is due
82
inherent spatial autocorrelation in river networks, the subtlety of the gradients being examined,
the small number of species being examined, natural environmental stochasticity and the non-
linearity of biotic responses to environmental variation (Demars & Harper 2005).
The higher percentage variation explained in this study over that of Matson (2006) and
McElarney & Rippey (2009) may be due to the smaller scale of this study and the smaller
gradient in physico-chemistry encountered. Studies on as small a scale as that undertaken in the
Milltown Lake catchment are not evident in the literature but the general trend is for larger
scale studies to be dominated by the response of communities to latitude (Heino et al. 2003),
altitude (Holmes et al. 1998) and geology (Riis et al. 2000). Regional scale studies will show
the impacts of land use and catchment management (Kuhar et al. 2007; Oťaheľová et al. 2007),
along with site physico-chemical characteristics (Grasmück et al. 1995). Such an increase in
explained variation as that presented here highlights the importance of local scale studies to
highlight local scale responses to water quality (Belle & Hughes 1983) and the increasing
importance of water quality variables in structuring macrophyte communities at decreasing
spatial scales. This suggests that the use of community responses to water quality should be
investigated at the scale at which it will be used. Relationships based on country or continent
wide datasets may over or under estimate the importance of certain variables at individual sites.
The consequences of such misinformation may include misclassification of water quality at
those sites.
3.6 Conclusions
3.6.1 To describe the macrophyte flora of a small drumlin catchment with
regard to relevant, comparable macrophyte typologies
The macrophyte community recorded in the current study is characteristic of the basic geology
and circum neutral waters of the Milltown lake catchment. Species abundance at most of the
sites suggests that these sites cannot be equated with larger lowland rivers, whose length can be
choked with macrophyte growth. This, and the dominance of the Bryophyta at the majority of
sites suggests that the macrophyte communities of these drumlin streams may be intermediate
between the upland headwater communities and large lowland communities described in the
83
literature. Such intermediate stream types may not be adequately represented in the Irish river
typology due to lack of reference condition sites. Consequences of the use of WFD tools at
these sites without adequate representation in the typology may include misclassification of
these sites with regard to water quality. Furthermore, the use of macrophyte classifications for
conservation purposes is common in Britain (Boon et al. 2002) and may gain more importance
in Ireland in assessing the conservation criteria for the WFD. Where classifications are used for
the assessment of conservation status, poor representation of certain types may also result in the
incorrect assignment of that status.
3.6.2 To assess the temporal variability in macrophyte taxon richness and
abundance in the catchment both within and between years
The analysis of temporal variation in the macrophyte community showed that there were no
significant differences between the taxon richness or abundance recorded at the sites either
between seasons or between years. These findings may be limited to small streams with larger
substrate sizes as some species associated with silty habitats are known to increase abundance
greatly over the growing season (Cotton et al. 2006). This suggests that macrophyte surveys of
the Milltown Lake catchment could compared with those of other surveys without impact from
temporal variation. This would place fewer restrictions on survey planning and may be of
particular use in such spate prone catchments as poor conditions could be avoided when
necessary without the fear of introducing temporal variation.
3.6.3 To provide a classification of sites on the Milltown lake catchment
based on the macrophyte community
Analysis of both bank and macrophyte datasets highlighted distinct groupings of sites
based on their macrophyte communities. This suggests that there is spatial variation in
the macrophyte community composition. It suggests that there is either a number of
drivers of macrophyte community composition or that there is one driver which is
effective along a gradient throughout the catchment.
84
Both the bank and the aquatic macrophyte datasets showed little relationship with the
dataset of seasonal physico-chemical parameters taken during the macrophyte surveys.
This may suggest that the macrophyte communities are not structured by the physico-
chemical parameters of the water column and that only the physical characteristics were
responsible for explaining variation. However, it may instead suggest that the snap shot
of water quality provided by these grab samples does not adequately explain the
variation in water quality in the catchment.
Analysis of the bank dataset showed that it was affected by a number of physical
characteristics related to river hydromorphology and river type (i.e. alkalinity (as a
surrogate for hardness) and slope). The relationship between the banks species and
hydromorphological characteristics may suggest they have a use in the assessment of
hydromorphological pressure. Bank species may be better suited to this role as they do
not appear to be affected by the independent pressures of eutrophication.
The aquatic macrophyte dataset was related to both physical and chemical
characteristics of the sites. Where the larger gradient structuring macrophyte
communities is the site physical characteristics, the effects of chemical determinands,
such as nutrient concentrations on the aquatic community may be more difficult to
assess. This may be of particular importance in the drumlin landscape as the undulating
topography results in rapid changes in site physical characteristics. If the response of
macrophytes to physico-chemical parameters is being assessed, sites should be as
physically similar as possible to limit any variation. Although the sites in the current
study shared a number of similar physical characteristics, a more stringent approach
should apply. This may be of wider importance where macrophyte surveys are being
added to existing monitoring programs, such as that carried out by the EPA. Sites which
are currently being sampled for macroinvertebrates may not be physically comparable
with each other. This may introduce error into the comparison of ecological status
between sites.
85
3.6.4 To highlight macrophyte community responses to physical and
chemical conditions in the Milltown Lake catchment
Banks species did not show any significant relationship with the longer term dataset of
physico-chemical parameters measured. It is possible that bank species might related
better to high flow physico-chemical conditions but that could not be confirmed from
this study. The lack of a relationship between the bank macrophyte dataset and the
physico-chemical parameters of the water quality suggest that additional information
will not be provided by collecting this data for the purposes of water quality monitoring.
TRP, NH4 and TDS concentrations from the longer term dataset of physico-chemical
parameters explained significant variation in the aquatic macrophyte community. The
relationship between the aquatic macrophyte dataset and the physico-chemical
parameters of the water quality suggest that nutrient concentrations play a part in
structuring the macrophyte community of the Milltown Lake catchment. However, the
amount of variation unexplained (~ 65 %) suggests there are other factors which are
exerting a stronger influence on the community.
The comparably large amount of variation in the aquatic macrophyte data explained by
the physico-chemical parameters may be due to effects related to the size and scale of
the study. This suggests that tools based on the use of community responses to water
quality should be investigated at the scale at which they will be used.
The better agreement between the aquatic macrophyte community and the longer term dataset
of annual median physico-chemical values suggests macrophyte community may be
representing responses to conditions over a longer period of time (Egertson et al. 2004;
Sondergaard et al. 2007). This may suggest that macrophytes might best be used in operational
monitoring programs where data on baseline water quality is desired rather than in investigative
monitoring where tools with rapid responses might be required.
86
3.6.5 To assess the ability of the bank species to differentiate between sites
and to respond to physico-chemical conditions at those sites by
comparison with the aquatic species
Classification analysis showed that there was variation in the bank macrophyte dataset
and that sites could be differentiated by their bank species. Variation in the bank
macrophyte dataset was explained by physical characteristics of the sites and may suggest
a role for bank macrophytes in the assessment of river hydromorphology. However,
ordination analysis showed that there was no significant relationship between the bank
macrophytes and the physico-chemical parameters of water quality. As such, the use of
bank macrophytes in surveys for water quality assessments is not recommended.
87
Chapter 4 Characteristics of Stream Bed Sediments in the Milltown
Lake Catchment
__________________________________________________________________
4.1 Introduction
Studies on the seasonal dynamics of stream bed sediments are few, as indeed are studies on any
facet of riverine sediments. The nature of river sediments are understood to be dynamic as the
transport of dissolved and suspended sediment material is an acknowledged function of rivers
(Vannote et al. 1980). General trends in these dynamics as well as the role of spate and flood
events are poorly understood (Clarke & Wharton 2001). Studies of sediment nutrients in Ireland
have mostly been concerned with standing water (e.g. Linnane 2001; Jordan & Rippey 2003;
Trodd 2003; Hobbs et al. 2005). Information available on the characteristics of stream bed
sediments in relation to biological communities has focused on the physical attributes of the
sediment such as dominant substrate size and is generally restricted to an estimation in situ
(O‘Leary & Breen 1998; Matson 2006; Callanan 2009). Characterisation of the chemistry of
stream bed sediments in relation to mining activities was carried out by the Environmental
Protection Agency (EPA) and assessed sediment metal concentrations (Ni Mhairtin et al. 1999).
A study was also carried out in the Avoca-Avonmore catchment to provide baseline conditions
for the assessment of metal contamination by acid mine drainage (Yau & Gray 2005). Neither
study analysed plant nutrient concentrations, but metal concentrations were shown to decrease
downstream of the mines. In the Avoca-Avonmore catchment cadmium (Cd) was only detected
close to where the mines were located and iron (Fe) was shown to be uniformly distributed, a
factor which was attributed to the underlying geology of the catchment. The EPA also funded a
study into stream bed sediment metal concentrations in relation to motorway run-off.
Interactions between metals and macrophytes were analysed by determining shoot and root
metal concentration in Apium nodiflorum L. at two river sites. Zinc (Zn) was found to
accumulate in significant concentrations in A. nodiflorum roots but Cd and copper (Cu) were
not (Bruen et al. 2006).
88
Iron (Fe) and manganese (Mn) are often included in studies of sediment chemistry due to their
ability to bind P in the sediment, allowing stream and lake sediments to act as sources and sinks
of available P (Jarvie et al. 2005; Dalton et al. 2010). Macrophyte communities have been
shown to respond to differences in geology between sites. Elements such as calcium (Ca),
magnesium (Mg) provide indications of the relative importance of such elements in the
surrounding geology. Furthermore, these elements, along with sodium (Na) have been shown to
affect dissolved organic carbon release by macrophytes (Wetzel 1969). Elements other than the
major nutrients N, P and K are of importance to macrophytes as trace nutrients. However, the
concentrations of these elements are also important due to potential toxic effects. These effects
include decreased biomass production (Lyngby & Brix 1984; Greger & Kautsky 1991), and
changes in root to shoot ratios and dieback of plants (Lakatos et al. 1999). The importance of
sediment metal concentrations to macrophytes is that uptake through the roots predominates in
most species (Cushing & Thomas 1980; Welsh & Denny 1980). Cu and Zn have been shown to
have such effects on macrophytes both in situ (Roussel et al. 2007) and in laboratory
experiments (Lyngby & Brix 1984; Greger & Kautsky 1991; Lakatos et al. 1999).
If macrophytes affect the sediment in which they are rooted then increased macrophyte biomass
later in the growing season may impact sediment characteristics (Cotton et al. 2006). Increased
agricultural activity has been linked to higher nutrient input rates to water and sediment
(Schippers et al. 2006). As agricultural activity increases during the summer months, effects
upon sediment nutrient chemistry may be expected. Baldy et al. (2007) found that shoot P
concentrations varied significantly overwinter in streams in Northeastern France, but sediment
P was not correlated to shoot P. Stutter et al. (2007) showed seasonal variations in % organic
carbon (C), the ratio of organic C to organic N and the ratio of organic C to bioavailable P
based on samples taken in May and samples taken in August in Scotland. Stephens et al. (1997)
found that there was a difference between total P (TP) concentrations between samples taken in
June and samples taken in August in the Norfolk Broads. This is in contrast with the findings of
Schneider & Melzer (2004) who found that there was no significant difference between nutrient
concentrations in samples taken in June/July and August/September in running waters in
Germany.
89
Due to the reported effects of macrophytes on the sediments in which they are rooted,
differences between the sediment physico-chemical characteristics of vegetated and non-
vegetated patches of the stream bed have been investigated by a number of authors (Stephen et
al. 1997; Clarke & Wharton 2001). Stephen et al. (1997) found that two separate lake sites in
the Norfolk Broads had sediment TP concentrations which were significantly higher in the
vegetated samples compared to those without vegetation. The findings of Clarke & Wharton
(2001) suggest that this does not hold true in the case of river sediments where there was no
significant difference found between the vegetated and non-vegetated samples collected, but
Wigand et al. (2001), also working on river samples, reported significantly higher P
concentrations in vegetated samples.
The use of macrophytes as indicators of enrichment in waters requires an understanding of the
nature of plant nutrient uptake. Aquatic macrophytes take in their nutrients both from the water
column (Seddon 1972) and from the sediment (Barko & Smart 1980; Chambers & Kalff 1987).
This use of both sediment and water column nutrients has been found to vary by species (Barko
& Smart 1980), with the relative concentrations of nutrients in the water and the sediment
(Carignan & Kalff 1980; Schneider & Melzer 2004) and along a gradient of pH (Schuurkes et
al. 1986). Nutrients added to the system can reside in the sediment (Stutter et al. 2007) and (for
shorter periods of time) in macrophyte stands after a particular enrichment event (Clarke 2002).
As a result sediment analysis may be necessary to understand macrophyte community responses
(Holmes 1999) and the timescales on which they might occur (Schorer et al. 2000; Barendregt
& Bio 2003).
4.2 Aims
1. To describe the stream bed sediment characteristics of the Milltown Lake catchment
2. To compare physico-chemical characteristics of sediment collected from vegetated and
non-vegetated patches of the stream bed
3. To compare physico-chemical characteristics of sediment collected in June and samples
collected in September
4. To assess the variation in sediment characteristics across the Milltown Lake catchment
90
4.3 Methods
4.3.1 Sediment Sampling and Analysis Methodology
Surficial sediment samples were collected during the June 2006 and September 2006
macrophyte survey periods. Five replicate samples were taken from vegetated areas and five
replicate samples were taken from non-vegetated areas within the macrophyte survey reach.
Sampling of sediment was not possible at some sites due to the nature of the substrate i.e.
boulder, gravel, cobble. Sampling of the surficial sediment layer was carried out using a plastic
scoop (Yau & Gray 2005; Bruen et al. 2006; Stutter et al. 2007) to a maximum depth of 10 cm.
Sediment was collected in a sealable plastic bag (Stutter et al. 2007). Upon return to the
laboratory, replicates were pooled into a single sample of vegetated and a single sample of non-
vegetated sediment for each site. An aliquot of wet sediment from each sample was weighed
and then oven dried overnight at 105 ºC. After drying samples were again weighed and wet and
dry weight calculations were carried out. Samples were then ashed in the furnace for four hours
at 450 ºC and once again weighed for organic matter (OM) determination by loss on ignition. A
further aliquot of air dried sediment was then used for particle size analysis. This was carried
out by hand-shaking the sediment for 3 minutes through increasingly small sieve apertures
(2000 µm, 63 µm) and weighing the portions of the sediment which remained in each (Gee &
Bauder 1986). Sediment chemical analysis was carried out on the fraction of sample smaller
than 2000µm, which was ground to pass through a 250 µm sieve (Svendsen et al. 1993; Clarke
& Wharton 2001). This ground material was then digested and analysed for nutrient and metal
content.
TP analysis was carried out by digestion of 0.2 g of ground sediment with 25 ml 1 M
hydrochloric acid by boiling for 15 minutes on a hot plate (Andersen 1976). The digest was
filtered through Whatman grade 50 filter paper and washed repeatedly with deionised water to a
final volume of 100 ml and analysed colorimetrically using the molybdate method of Murphy &
Riley (1962). Samples were analysed in triplicate. Blanks and standards were prepared in the
same way. After colour development, absorbance was read on a Gensys 6 spectrophotometer at
680 nm using a 1 cm path length. The limit of detection was 0.005 mg/L. A further 0.2 g of
ground sediment was digested with 2 ml of nitric/hydrochloric (4:1) acid in a Teflon ―bomb‖
91
for four hours. The ―bombs‖ were then put in an oven for 22 hours. The digest was filtered
through Whatman grade 50 filter paper and washed repeatedly with deionised water to a final
volume of 10 ml. Blanks and standards were prepared in the same way. Samples were analysed
for Cu, Fe, Mn, Mg, K, Na, Zn and Ca using atomic absorption spectrometry (AAS) on a
Varian Spectra AA flame spectrophotometer at the wavelengths outlined in Table 4.1. Standard
curves were generated for each element over the entire detection range using the appropriate
commercial standards. TN was analysed using the Kjeldahl method by digestion with boric acid
on a Buchi B-435 digestion unit and a Buchi B-316 Distillation Unit and titration with 0.1 M
hydrochloric acid (APHA 1995). The percentage organic carbon was estimated from the OM
content by calculation using the Von Bemmelen factor (Nelson & Sommers 1996). Values for
all parameters are as dry weight and are presented in Appendix 3.
Table 4.1 Wavelengths used and sample preparation for AAS analysis of sediment samples
Element Wavelength (nm) Dilution of
original extract
Range for standard curve
(mg l-1
)
Cu 324.7 0 0.25-10
Fe 248.3 40 1-15
Mg 285.2 160 0.25-1
Mn 279.5 40 1-5
K 766.5 160 0.03-1.5
Ca 422.7 40 0.5-2.5
Zn 213.9 0 0.1-2
Na 589.0 640 0.05-1
4.3.2 Statistical Analysis
All sediment data were normalised using a log(x) transformation. Pearson‘s correlation analyses
were carried out in SPSS 15.0 to assess the relationship between the sediment characteristics. T-
tests were carried out in SPSS 15.0 to show differences between vegetated and non-vegetated
sediment and differences in sediment chemistry between seasons. Hierarchical clustering was
performed on a combined dataset of all sites and all seasons in Primer v6 to highlight
relationships between sites based on sediment characteristics. These relationships were then
confirmed using non-metric MultiDimensional Scaling (MDS) (Clarke & Gorley 2006).
92
4.4 Results
4.4.1 Catchment Sediment Characteristics
Sediment physico-chemical characteristics for all sites and dates combined are summarised in
Table 4.2 below. Data for individual sites and sampling occasions are given in Appendix 3.
Surficial sediment could not be sampled at the sites TH5 and TH6 in June 2006 or at sites TV6,
GO3, GO4, GO5, TH6, MA2, MA4, DG1 or CN1 in September 2006. This was due to the lack
of suitable substrate at those sites.
The sand fraction dominated the majority of sediment samples with silt/clay comprising only
1.34 % of the samples on average. The maximum value of 12.5 % was at the site TH2 in
September 2006 (Figure 4.2a). Twenty five of the samples analysed had no silt/clay fraction.
The range of TP measured in the sediments from the Milltown Lake catchment was between 0.1
and 0.57 mg/g (Table 4.2). The mean TP concentration for the samples was 0.24 mg/g. The site
with the highest TP concentration in June was CN1 (Figure 4.1c). However, there was no
sample taken for that site in September due to lack of suitable substrate. The highest recorded
TP concentration in September 2006 was at D2 (Figure 4.2c). Fe:P values may suggest further
capacity for P binding within the surficial sediments. The highest Fe:P value was at DG1 in
September 2006 (Figure 4.2d).
There was little variation in the % TN values for the sites, the range was 0.03 % to 1.11 % and
the mean value was 0.22 %. The site with the highest % TN in June 2006 was MA4 (Figure
4.1e). There was no sample taken for that site in September due to lack of suitable substrate.
The site with the highest % TN in September was D2 (Figure 4.2e). The range of values for %
OM for the sites of the Milltown Lake catchment was 10.02 to 67.65 %. The mean value for the
catchment was 20.27 % (Table 4.2). The site with the highest % OM in June 2006 was GO4
(Figure 4.1f), there was no sample taken for September when the site with the highest % TN
was TV1 (Figure 4.2f). The % C was derived from the % OM by calculation, the mean value
for the catchment was 11.76 % and the range of values was 5.81 to 39.24 % (Table 4.2). The
mean concentrations of elements analysed by AAS were found to be (in decreasing order)
Na>Fe>Mg>Ca>K>Mn>Cu>Zn.
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Table 4.2 Summary statistics for sediment physico-chemical characteristics for all sites and all
seasons. All values are as dry weight
Mean Std. Error Minimum Maximum Range N
% Sand 98.61 0.26 87.50 100.00 12.50 60
% Silt/Clay 1.39 0.26 0.00 12.50 12.50 60
TP mg/g 0.24 0.01 0.10 0.57 0.46 56
Fe:P 3.10 0.12 1.04 5.27 4.23 56
% TN 0.22 0.02 0.03 1.11 1.08 60
% OM 20.27 1.14 10.02 67.65 57.63 60
% C 11.76 0.66 5.81 39.24 33.43 60
C:N 73.99 5.66 9.44 230.41 220.97 60
Mn mg/g 0.08 0.01 0.00 0.22 0.22 60
Mg mg/g 0.24 0.005 0.12 0.32 0.20 60
Ca mg/g 0.15 0.01 0.03 0.25 0.22 60
Fe mg/g 0.71 0.03 0.25 1.79 1.54 60
K mg/g 0.11 0.01 0.01 0.25 0.24 60
Na mg/g 0.84 0.04 0.37 1.87 1.50 60
Zn µg/g 17.99 1.47 2.10 51.00 48.90 60
Cu µg/g 18.32 0.90 1.75 31.63 29.88 60
94
a)
90
91
92
93
94
95
96
97
98
99
100
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Sa
nd
NV
V
c)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
TP
mg
/g
NV
V
b)
0
1
2
3
4
5
6
7
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Sil
t/C
lay
NV
V
d)
0
1
2
3
4
5
6
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Fe
:P NV
V
Figure 4.1 Bar charts of a) % Sand, b) % Silt/Clay, c) TP mg/g, d) Fe:P for vegetated (V) and non-vegetated (NV) sediment
samples at each site for June 2006. All values are as dry weight
95
e)
0%
10%
20%
30%
40%
50%
60%
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
To
tal
Nit
rog
en
NV
V
g)
0
5
10
15
20
25
30
35
40
45
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Ca
rbo
n
NV
V
f)
0
10
20
30
40
50
60
70
80
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Org
an
ic M
att
er
NV
V
h)
0
50
100
150
200
250
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
C:N
NV
V
Figure 4.1 Bar charts of e) % TN, f) % OM, g) % C, h) C:N for vegetated (V) and non-vegetated (NV) sediment samples at each
site for June 2006. All values are as dry weight
96
i)
0
0.05
0.1
0.15
0.2
0.25
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Mn
mg
/g
NV
V
k)
0
0.05
0.1
0.15
0.2
0.25
0.3
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Ca
mg
/g
NV
V
j)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Mg
mg
/g
NV
V
l)
0
0.2
0.4
0.6
0.8
1
1.2
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Fe
mg
/g
NV
V
Figure 4.1 Bar charts of i) Mn mg/g, j) Mg mg/g, k) Ca mg/g, l) Fe mg/g for vegetated (V) and non-vegetated (NV) sediment
samples at each site for June 2006. All values are as dry weight
97
m)
0
0.05
0.1
0.15
0.2
0.25
0.3
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
K m
g/g NV
V
o)
0
10
20
30
40
50
60
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Zn
ug
/g
NV
V
n)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Na
mg
/g
NV
V
p)
0
5
10
15
20
25
30
35
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Cu
ug
/g
NV
V
Figure 4.1 Bar charts of m) K mg/g, n) Na mg/g, o) Zn µg/g, p) Cu µg/g for vegetated (V) and non-vegetated (NV) sediment
samples at each site for June 2006. All values are as dry weight
98
a)
80
82
84
86
88
90
92
94
96
98
100
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Sa
nd
NV
V
c)
0
0.1
0.2
0.3
0.4
0.5
0.6
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
TP
mg
/g
NV
V
b)
0
2
4
6
8
10
12
14
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Sil
t/C
lay
NV
V
d)
0
1
2
3
4
5
6
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Fe
:P NV
V
Figure 4.2 Bar charts of a) % Sand, b) % Silt/Clay, c) TP mg/g, d) Fe:P for vegetated (V) and non-vegetated (NV) sediment
samples at each site for September 2006. All values are as dry weight
99
e)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
To
tal
Nit
rog
en
NV
V
g)
0
5
10
15
20
25
30
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Ca
rbo
n
NV
V
f)
0
10
20
30
40
50
60
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Pe
rce
nta
ge
Org
an
ic M
att
er
NV
V
h)
0
20
40
60
80
100
120
140
160
180
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
C:N
NV
V
Figure 4.2 Bar charts of e) % TN, f) % OM, g) % C, h) C:N for vegetated (V) and non-vegetated (NV) sediment samples at each
site for September 2006. All values are as dry weight
100
i)
0
0.05
0.1
0.15
0.2
0.25
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Mn
mg
/g
NV
V
k)
0
0.05
0.1
0.15
0.2
0.25
0.3
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Ca
mg
/g
NV
V
j)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Mg
mg
/g
NV
V
l)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Fe
mg
/g
NV
V
Figure 4.2 Bar charts of i) Mn mg/g, j) Mg mg/g, k) Ca mg/g, l) Fe mg/g for vegetated (V) and non-vegetated (NV) sediment
samples at each site for September 2006. All values are as dry weight
101
m)
0
0.05
0.1
0.15
0.2
0.25
0.3
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
K m
g/g NV
V
o)
0
10
20
30
40
50
60
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Zn
ug
/g
NV
V
n)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Na
mg
/g
NV
V
p)
0
5
10
15
20
25
30
35
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6
TH1
TH2
TH3
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
Site Code
Cu
ug
/g
NV
V
Figure 4.2 Bar charts of m) K mg/g, n) Na mg/g, o) Zn µg/g, p) Cu µg/g for vegetated (V) and non-vegetated (NV) sediment
samples at each site for September 2006. All values are as dry weight
102
Correlation analyses were carried out on all sites and sampling periods combined to
characterise the relationships between the different physico-chemical parameters measured in
the surficial sediments (Table 4.3). These relationships were plotted to assess their linearity and
are presented in Figure 4.1.
% Silt/Clay was significantly positively correlated with Zn (r=0.358, p=0.035). Although
significant, this correlation was weak. It can be seen from the plot in Figure 4.1 that this
relationship may be based on a few samples with a high percentage of silt/clay. The TP
concentration was significantly positively correlated with % TN, Mn, Mg, Fe, K and Zn
(r=0.452, p=0.000, r=0.549, p=0.000, r=0.385, p=0.003, r=0.591, p=0.000, r=0.278, p=0.038
and r=0.335, p=0.012 respectively). TP was also significantly negatively correlated with Fe:P
and C:N (r=-0.572, p=0.000 and r=-0.436, p=0.001 respectively). Fe:P was significantly
negatively correlated with % OM (r=-0.272, p=0.042).
% TN was significantly positively correlated with % OM, Mn, Fe and Na (r=0.475, p=0.000,
r=0.272, p=0.036, r=0.280, p=0.030 and r=0.256, p=0.049 respectively). However, these
relationships seem to be dominated by a few high % TN samples (Figure 4.1). % OM was
significantly negatively correlated with Mg (r=-0.314, p=0.014). C:N was significantly
negatively correlated with Fe (r=0.377, p=0.003). Mn was significantly positively correlated
with Mg, Fe, K and Zn (r=0.479, p=0.000, r=0.678, p=0.000, r=0.362, p=0.005 and r=0.312,
p=0.015 respectively). Mg was significantly positively correlated with Fe and K (r=0.580,
p=0.000 and r=0.287, p=0.026) and significantly negatively correlated with Cu (r=-0.270,
p=0.037). Fe was significantly positively correlated with Zn (r=0.401, p=0.002) and
significantly negatively correlated with Cu (r=-0.259, p=0.046). However, this correlation was
based on one outlier (Figure 4.1). K was significantly positively correlated with Na and Cu
(r=0.322, p=0.012 and r=0.266, p=0.040).
103
Figure 4.1 Draftsman‘s plot of combined sediment physico-chemical parameters for sites of the Milltown Lake catchment for June
2006 and September 2006
104
Table 4.3 Correlation matrix for sediment physico-chemical parameters for all sites and all seasons. Asterisked values highlight
significant results. All values are as dry weight
%Sand %
Silt/Clay
TP
mg/g
Fe:P %TN %OM C:N Mn
mg/g
Mg
mg/g
Ca
mg/g
Fe
mg/L
K
mg/L
Na
mg/g
Zn
µg/g
%Sand r
p
%Silt/Clay r -1.000
p 0.000*
TP mg/g r 0.308 -0.308
p 0.021* 0.021*
Fe:P r -0.281 0.281 -0.579
p 0.036* 0.036* 0.000*
%TN r 0.208 -0.208 0.466 -0.414
p 0.112 0.112 0.000* 0.001*
%OM r 0.209 -0.209 0.362 -0.429 0.596
p 0.108 0.108 0.006* 0.001* 0.000*
C:N r -0.092 0.092 -0.393 0.276 -0.835 -0.145
p 0.486 0.486 0.003* 0.040* 0.000* 0.270
Mn mg/g r 0.228 -0.228 0.534 0.040 0.280 0.158 -0.238
p 0.080 0.080 0.000* 0.771 0.030* 0.228 0.067
Mg mg/g r -0.082 0.082 0.310 0.163 -0.162 -0.171 0.007 0.408
p 0.534 0.534 0.020* 0.231 0.217 0.191 0.957 0.001*
Ca mg/g r 0.180 -0.180 0.296 -0.307 0.255 0.204 -0.178 0.087 -0.063
p 0.170 0.170 0.027* 0.021* 0.049* 0.118 0.173 0.511 0.630
Fe mg/g r 0.058 -0.058 0.608 0.170 0.157 0.002 -0.221 0.715 0.575 0.131
p 0.660 0.660 0.000* 0.209 0.230 0.991 0.090 0.000* 0.000* 0.319
K mg/g r 0.106 -0.106 0.244 -0.262 0.091 0.061 -0.039 0.377 0.206 -0.170 0.150
p 0.421 0.421 0.070 0.051 0.489 0.641 0.768 0.003* 0.114 0.193 0.254
Na mg/g r 0.069 -0.069 0.159 -0.172 0.260 0.135 -0.293 -0.027 -0.135 -0.009 0.016 0.357
p 0.598 0.598 0.243 0.206 0.045* 0.303 0.023* 0.840 0.304 0.944 0.901 0.005*
Zn µg/g r 0.003 -0.003 0.440 -0.085 0.159 0.199 -0.123 0.419 0.226 0.145 0.428 -0.033 -0.060
p 0.984 0.984 0.001* 0.532 0.225 0.128 0.351 0.001* 0.083 0.269 0.001* 0.803 0.650
Cu µg/g r 0.110 -0.110 -0.100 -0.224 0.032 0.010 -0.015 0.053 -0.203 0.087 -0.113 0.194 0.007 -0.046
p 0.403 0.403 0.464 0.097 0.811 0.939 0.911 0.687 0.120 0.507 0.390 0.137 0.955 0.729
105
4.4.2 Sediment Variability between Vegetated and Non-vegetated Patches
To assess the variability of sediment physico-chemical characteristics between samples taken
from vegetated and non-vegetated areas, data from both the June 2006 and September 2006
datasets were amalgamated. The t-tests showed that only one parameter was significantly
different, Cu (t=2.511, p=0.019) (Table 4.4). Cu concentrations were significantly higher in
non-vegetated sediments when compared to vegetated sediments (Figure 4.3a).
4.4.3 Sediment Variability between Seasons
To assess the variability of sediment physico-chemical characteristics between samples taken
during June 2006 and September 2006 data from both the vegetated and non-vegetated datasets
were amalgamated. Again, t-tests were used (Table 4.4). The concentration of K in the sediment
was the only measured parameter which changed significantly between seasons (t=3.509,
p=0.001) with K concentrations decreasing between June 2006 to September 2006 (Figure
4.3b).
a)
b)
Figure 4.3 Boxplots of a) Cu concentrations in vegetated and non-vegetated sediments and b)
sediment K concentrations in June 2006 and September 2006
106
Table 4.4 T-test statistics for a) differences between vegetated and non-vegetated sediments and b) differences between seasons (June
2006 and September 2006). Degrees of freedom have been corrected for unequal variances. Asterisked values highlight significant
results
a)
t df p
% Sand -0.358 57.864 0.721
% Silt/Clay -0.162 32.355 0.872
TP mg/g 0.563 38.570 0.576
Fe:P -1.295 38.875 0.203
% TN 1.084 56.216 0.283
% OM 1.572 46.372 0.123
% C 1.572 46.372 0.123
C:N -0.256 57.901 0.799
Mn mg/g 0.261 42.748 0.795
Mg mg/g 0.489 47.730 0.627
Ca mg/g 0.389 32.913 0.699
Fe mg/g -0.378 41.514 0.708
K mg/g 0.976 28.960 0.337
Na mg/g 0.693 25.315 0.494
Zn µg/g 1.329 27.107 0.195
Cu µg/g 2.511 23.478 0.019*
b)
t df p
% Sand 0.576 32.165 0.569
% Silt/Clay 0.423 23.249 0.676
TP mg/g 0.958 44.710 0.343
Fe:P -1.870 52.390 0.067
% TN -0.373 44.176 0.711
% OM 0.909 46.362 0.368
% C 0.909 46.362 0.368
C:N 0.918 39.503 0.364
Mn mg/g 1.277 42.069 0.208
Mg mg/g 1.134 39.387 0.264
Ca mg/g -1.962 57.781 0.055
Fe mg/g -0.680 45.396 0.500
K mg/g 3.509 45.668 0.001*
Na mg/g -0.349 46.205 0.729
Zn µg/g -0.591 57.795 0.557
Cu µg/g -0.832 57.998 0.409
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4.4.4 Relationships between Sites based on Sediment Physico-Chemical
Characteristics
Although the CLUSTER and MDS analyses includes data from the same sites (replicated
during different seasons and from vegetated and non-vegetated areas), samples did not cluster
together based on site, season or vegetation status. This supports the findings of the t-tests in
Sections 4.4.2 and 4.4.3, which found that differences between seasons and vegetation status
were not significant. Most of the sites clustered together in a large group suggesting that overall
variability in the sediment samples was low. It can also be seen from the cluster analysis
(Figure 4.4) that sites did not cluster together based on parent tributary i.e. they were more
similar to sites from other tributaries than sites up or downstream.
The first division of the CLUSTER dendrogram separated the sites D1, D2, TV1, MA2, MA4,
DG1, DG2 and CN1 from the rest of the sites at the greatest distance (Figure 4.4). It can be seen
from the dendrogram that although they are significantly different from the rest of the sites,
they are also placed further from each other on the MDS plot than any of the other sites which
are in significantly similar clusters (Figure 4.5). The sediment characteristics of this group
included low % TN and resulting high C:N ratios. The site TH2 was divided from the rest of the
sites at the next division of the dendrogram. TH2 had the highest % Silt/Clay (12.5%) recorded
for each of the seasons (Appendix 3). The next site to be separated from the rest was TH3, this
site had the highest Cu concentration (31.62 µg/g) recorded for each of the seasons (Appendix
3). Both GO6 and D2 clustered together at the next lowest average similarity, these sites were
among the largest in the catchment and had the two highest Zn concentrations recorded (49.49
and 51 µg/g respectively). The sites GO1 and GO4 share similar particle size fractions and
similar Zn concentrations (Appendix 3).
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Figure 4.4 Cluster dendrogram of sites by sediment physico-chemical characteristics. Site codes are followed by a season code (J06 =
June 2006, S06 = Sept. 06) and a vegetation status code (V = vegetated, NV = Non-vegetated). Coloured symbols show membership of
cluster groups
109
Figure 4.5 MDS of sites by sediment physico-chemical characteristics. Sites are colour coded
based on the groups from the dendrogram in Figure 4.4 above
110
4.5 Discussion
Characterisation of sediment in Ireland has mostly been carried out in lentic environments, with
few studies looking at riverine sediments. Those studies that have concerned themselves with
riverine sediments have been focused on the impacts of either mining (Ni Mhairtin et al. 1999;
Yau & Gray 2005) or motorways (Bruen et al. 2006) and so, there are few studies against which
to compare the results of the current study. There have been more numerous studies in the UK
where Garcia-Ruiz et al. (1998) examined the relationship between denitrification and both
water and sediment chemistry, Clarke & Wharton (2001) examined the role of sediment
nutrients in structuring macrophyte communities in England, Jarvie et al. (2005) examined the
role of sediments as sources or sinks of soluble reactive P in England and Stutter et al. (2007)
examined the relationship between sediment and macroinvertebrate water quality indices in
Scotland. In addition, trace element concentrations of riverine sediments have been examined in
France (N‘guessan et al. 2009). However, all of these studies were conducted across larger
watersheds than the results presented here and so were subject to larger gradients in sediment
physico-chemical characteristics. In Denmark much work has been done on the interactions
between macrophytes and sediment (e.g. Sand-Jensen et al. 1982; Sand-Jensen & Mebus 1996;
Sand-Jensen 1998). However, these studies were carried out at either one site or were
laboratory based, limiting the ability to compare those results to the current study.
The TP concentrations measured in the sediments from the Milltown Lake catchment (0.1 –
0.57 mg/g) were within the range reported by Clarke & Wharton (2001) for lowland rivers in
England (Table 4.5). These concentrations were also, for the most part, below the 0.5 mg/g
concentration suggested by those authors as a range for rivers of low P concentrations. This
suggests that the TP concentrations of the sites of the Milltown Lake catchment are low. The
TP concentrations found in the current study are also of the same order of magnitude of those
found by Hobbs et al. (2005) in the inflows to Lough Carra, Co. Mayo, but are lower than those
reported by Carson (2011) for Milltown Lake itself (Table 4.5). Due to the depositional nature
of lakes and the accumulation of sediment over long periods of time, Clarke & Wharton (2001)
suggest that comparisons between lake and river sediment nutrient concentrations would result
in the classification of river sediments as less productive. This was not found to be the case in
111
the inflows to Lough Carra (Hobbs et al. 2005) and in fact mean concentrations in the streams
of the Milltown Lake catchment were in excess of those reported for the mid and south basins
of that lake. Squires & Lesack (2003) also reported similar P and N concentrations for samples
from riverine and lacustrine sediment in the Mackenzie Delta, Canada (Table 4.5). Mean TP
concentrations for the Milltown Lake catchment were also in excess of those reported by Trodd
(2003) for two lakes in the flooded peatlands at Turraun, Co. Offaly. However, the maximum
concentration obtained in that study (1257.6 µg/g) (Table 4.5) was double the maximum
concentration obtained in the current study and so shows wider variations than the results
presented here. Results presented here were lower than those presented by Dalton et al. (2010)
for five lakes in Ireland (Table 4.5).
Table 4.5 Ranges of TP and TN presented in various studies on sediment characteristics
Study Location TP range TN range
Current study Milltown Lake
catchment
0.1 – 0.57 mg/g 0.03 to 1.11 %
(Clarke & Wharton
2001)
17 lowland rivers in
England
35 – 2660 µg/g 0.02 - 0.54 %
(Squires & Lesack
2003)
Mackenzie Delta 767 - 1705 µg/g 200 - 14000 µg/g
(Trodd 2003) Turraun, Co Offaly 2.3 – 1257.6 µg/g Not analysed
(Johnson &
Ostrofsky 2004)
Lake Pleasant,
Pennsylvania
BLD – 3.09 %
(Hobbs et al. 2005) Lough Carra, Mayo 0.1 – 0.54 mg/g Not reported
(Carson 2011) Milltown Lake 1.244 mg/g 7.84 mg/g
(Dalton et al. 2010) Selected Irish lakes 0.5 – 2 mg/g ~ 10 mg/g
(Bunaveela only)
High variability in % TN was reported by Johnson & Ostrofsky (2004) in Lake Pleasant,
Pennsylvania (Table 4.5), but % TN for sites in British rivers were not highly variable (Clarke
& Wharton 2001). The range of % TN in the Milltown Lake catchment was 0.03 to 1.11. While
the lower value is within the range found by Clarke & Wharton (2001), the upper value is
higher than the maximum value found by those authors. The mean value for %TN in the
Milltown Lake catchment (0.22 %) was also twice the mean concentration reported by Clarke
& Wharton (2001). % TN values were not reported by Hobbs et al. (2005) although TN was
analysed, N values are instead reported as a C:N ratio. The mean value for % OM for the
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Milltown Lake catchment (20.27 %) was higher than that found in English rivers (5.64 %), with
most sites exhibiting values an order of magnitude higher than those for the Hiz River in
Hertfordshire, a chalk and limestone river (Clarke & Wharton 2001). Values were also higher
than those found by Yau & Gray (2005) in the Avoca-Avonmore catchment (1.9 - 15.9 %). This
may be due to the nature of the allochthonous inputs from the surrounding catchment at
Milltown, which includes peatland (Chapter 2), a substrate rich in OM. Agricultural practices in
the catchment may also contribute to the high OM content due to inputs of organic waste
(Carson 2011). This is in agreement with the higher % TN recorded in this study as % TN and
% OM have been shown to be related (Sand-Jensen 1998). Strong correlations were also found
between % OM and % C by Hobbs et al. (2005) (r=0.959, p=0.01) and Clarke & Wharton
(2001) (r=0.820, p=0.01) who derived their % C by quantitative methods. Although % OM
levels were considerably lower in the study of Clarke & Wharton (2001) and % TN was higher,
both studies showed strong correlations between % TN and % OM.
The mean concentrations of elements analysed by AAS was found to be (in decreasing order)
Na>Fe>Mg>Ca>K>Mn>Cu>Zn. There are few analyses of sediment metal concentrations in
Ireland with which to compare the results of the current study. Those with which comparison
can be made suggest that metal concentrations in the Milltown Lake catchment are either lower
than or within the ranges reported by those studies. Zn and Cu concentrations were greatly
below those found by Yau & Gray (2005) in the Avoca, although this was to be expected as
those authors sampled downstream of a mine drainage network. Mn and Mg concentrations
were in the range and Ca concentrations were below the range of those reported by Hobbs et al.
(2005). Mn and Mg concentrations were lower than those reported by N‘guessan et al. (2009)
for the Gascogne River in France and K concentrations are below those reported by Johnson &
Ostrofsky (2004) for Lake Pleasant, Pennsylvania.
4.5.1 Sediment Variability between Vegetated and Non-vegetated Patches
The ability of aquatic macrophytes to affect flow (Chambers et al. 1991; Sand-Jensen & Mebus
1996; Sand-Jensen 1998; Cotton et al. 2006), sedimentation rate (Sand-Jensen 1998; Rooney et
al. 2003; Schulz & Köhler 2006) and sediment physical and chemical composition (Svendsen &
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Kronvang 1993; Clarke 2002) suggests that differences might be apparent between sediments in
which plants are rooted and those in which they are not. In fact, significant differences between
sediment physico-chemical variables from samples taken from vegetated patches and non-
vegetated patches has been shown for lake macrophyte – sediment interactions (Stephen et al.
1997). However, a study on differences between vegetated and non-vegetated sediment
physico-chemical parameters in English rivers (Clarke & Wharton 2001) found no significant
differences between the two populations for any of the six parameters measured (TP, inorganic
P, % TN, organic C, % OM and % silt/clay). The results of the current study are broadly in
agreement with those of Clarke & Wharton (2001), although Cu concentrations were
significantly higher in non-vegetated sediments when compared to vegetated sediments in the
Milltown Lake catchment. Cu is an essential nutrient for plant growth and development, but it
is a micro nutrient and is required only in small amounts. In fact Cu has been widely used as an
aquatic herbicide and concentrations in excess of 2 ppmw have been shown to inhibit growth
(Young & Lisk 1972; Anderson et al. 1987). Sediment Cu concentrations in the catchment form
part of a band of naturally high concentrations across the east of the country (Salminen 2005)
but are lower than the Interim Sediment Quality Guideline set out by the Canadian
Environmental Quality Guidelines (http://ceqg-rcqe.ccme.ca) (35.7 mg/kg). This suggests that
they are not of high enough concentration to affect aquatic communities. As such the reason for
the significant difference between Cu concentrations between vegetated and unvegetated
sediment remains unclear.
4.5.2 Sediment Variability between Seasons
Variability in sediment physico-chemical characteristics on a seasonal basis has been recorded
in the literature (Stephen et al. 1997; Stutter et al. 2007). Stutter et al. (2007) reported large
temporal variation between % organic C, organic C to organic N ratios and organic C to
bioavailable P ratios in sediments between samples taken in May 2004 and August 2004 from
the River Dee in Scotland. Although mean concentrations of the P, N and C fractions measured
in the Milltown Lake catchment were lower in September 2006 than in June 2006, these
differences were not found to be significant by the Mann Whitney U tests. This is further
evidenced by the clustering of sites in the CLUSTER and MDS plots, which showed that sites
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did not cluster together based on seasonal replicates. This may suggest that in contrast to lake
sediment, riverine sediments nutrient concentrations may be continually replenished by
constantly flowing pore water (Schneider & Melzer 2004; Lampert & Sommer 2007). The only
sediment physico-chemical characteristic which exhibited significant differences between June
2006 and September 2006 was K. The concentration of K decreased from June 2006 to
September 2006. The role of K as a macronutrient for plant protein synthesis assures its
importance in macrophyte growth, however it is thought that the primary source of K for
aquatic macrophytes is the water column (Barko et al. 1991) and not the sediment. K has been
suggested as a surrogate variable for available sediment N by Anderson & Kalff (1988) who
also found significant variation in sediment K between the spring and the end of the growing
season in ten lakes near Quebec. The relationship between K and N may be due to exchange of
potassium for ammonium ions at the roots during nitrogen limitation (Barko & Smart 1981;
Barko et al. 1988). Higher concentrations of K coincided with lower concentrations of N in
June 2006 and vice versa in September 2006. However, the change in N concentrations between
the two sampling periods was not significant and the use of K as a proxy for sediment N could
not be confirmed from this study. Low temporal variability in sediment characteristics was
found by Schneider & Melzer (2004) in rivers in Germany, leading to the conclusion that one
sampling period during the growing season was sufficient to characterise the sediment of those
streams. This is in agreement with the methodology taken by the Tellus project in Northern
Ireland (http://www.bgs.ac.uk/gsni/tellus) which aims to characterise the geochemistry of first
and second order streams through the use of one sample per stream.
4.5.3 Relationships between Sites based on Sediment Physico-Chemical
Characteristics
Hierarchical clustering analysis and ordination showed that many of the sites in the Milltown
Lake catchment were not well differentiated based on their sediment physico-chemical
characteristics. This agreement between sites based on sediment physico-chemical
characteristics may not be unexpected as the streams originate from within a small geographical
area and are subject to similar geology (Yang & Rose 2005), hydrology (Cooper 1990) and
landuse (Stutter et al. 2007). Large scale (e.g. regional or national) characterisation of stream
115
bed sediments has not yet been carried out in Ireland. Studies carried out at national (Clarke &
Wharton 2001) and regional (Stutter et al. 2007) scales in Britain found that different rivers and
sub-catchments were differentiated based on sediment physico-chemical characteristics.
Agreement between sites in the Milltown Lake catchment may be due to their similar geology,
hydrology and landuse and may support the findings of Stutter et al. (2007) that spatial
variation in sediment physico-chemical characteristics can be attributed to catchment scale
variables such landuse. It could also be seen from the cluster analysis that sites did not cluster
together based on parent tributary and so spatial variation was not attributed to differences
between the streams or their sources at this scale.
4.6 Conclusions
4.6.1 To describe the stream bed sediment characteristics of the Milltown Lake
catchment
Sediment TP concentrations were within the range reported by Clarke & Wharton (2001) for
low P rivers. OM and TN concentrations were at the higher end of the range published for
rivers. The Fe:P ratios suggest that the sediments retain potential buffering capacity against
future increases in P to the system. Stream sediment metal concentrations were within the
ranges presented by other studies. Cu concentrations were relatively high and are part of a band
of high sediment Cu across the east of the country. The relatively low P concentrations, coupled
with the high N concentrations may suggest that the sediment of the Milltown Lake catchment
is P limited. If the sediment retains buffering capacity against future increases in P, the
sediment may act as a sink of P. Retention of P in the sediment will allow concentrations in the
water column to remain low, however, P incorporated into the sediment will be available for
macrophyte growth.
4.6.2 To compare physico-chemical characteristics of sediment collected from
vegetated and non-vegetated patches of the stream bed
Variation in sediments due to the presence or absence of vegetation was not found to be
significant for the majority of the parameters measured. There was one exception, Cu
116
concentrations were higher in non-vegetated sediment. The implications of this finding remain
unclear, Cu concentrations were not thought to be high enough to limit macrophyte growth and
macrophytes do not utilised Cu in high enough concentrations to deplete the sediment reserves.
4.6.3 To compare physico-chemical characteristics of sediment collected in June
and samples collected in September
Temporal variation was also not found to be significant for the majority of the sediment
parameters measured. The exception to this was K. While seasonal dynamics between N and K
have led others to suggest the use of K as a proxy for N in sediment (Anderson & Kalff 1988),
the change in N concentrations between the two sampling periods in the current study was not
significant. As a result the use of K as a proxy for sediment N could not be confirmed from this
study.
4.6.4 To assess the variation in sediment characteristics across the Milltown Lake
catchment
Classification analyses showed little spatial variation in the sediment characteristics between
the sites in the Milltown Lake catchment. This may suggest that catchment scale processes are
important in governing river sediment characteristics. Low variation in sediment characteristics
may suggest that sediment characteristics will not play a significant role in structuring
macrophyte community composition across the catchment.
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Chapter 5 Community Concordance Analyses between the Macrophyte
Flora and Macroinvertebrate Fauna of Milltown Lake Catchment
_________________________________________________________________
5.1 Introduction
Communities are concordant when they respond similarly, but independently to the same
pressures (Paszkowski & Tonn 2000). One community might be able to act as a surrogate for
another in water quality and conservation assessments if it can be shown that the communities
in question are concordant (Paavola et al. 2003). Application and reliance on one community
will require less time and expertise for analysis and interpretation and therefore less money.
Concordance between two communities might be expected where both provide information on
the impacts of the same pressures through the use of water quality indices. However, as water
quality indices are based on a subset of indicator species and assessment of concordance is
based on a wider section of the community, they may not necessarily agree. Cosmopolitan
species might not contribute a water quality index, but will affect concordance analyses.
Conversely, while the presence of rare taxa may not affect overall classifications and
ordinations for a community (Marchant 2002), if they are indicator taxa they may impact the
water quality index greatly. Mykrä et al. (2008a) and Heino et al. (2005) found that
concordance between communities increased where water quality was lowest. Where
assessments on a number of communities must be integrated e.g. for Water Framework
Directive monitoring, knowledge of community concordance may aid understanding where
different communities assign different status to a water body (Moss et al. 2003) and where
communities are concordant, add confidence to the assessment (Hatton-Ellis 2008).
Concordance has been measured in the literature in a number of ways and increasing interest in
community concordance has led to the development of specific statistical analyses. The
simplest concordance analyses have been carried out on species richness indices, using non-
parametric uni- and bivariate tests to detect differences between sample means (Rios & Bailey
2006; Smith et al. 2007). Heino et al. (2009b) used a non-parametric Mantel-type test, which
118
carries out correlations on the community assemblage rather than on richness indices, thus
providing meaningful information on communities, even those that are species poor (Bilton et
al. 2006). More complex analyses have been used based on the classifications produced by
clustering analysis (e.g. Twinspan, Primer‘s CLUSTER). The cluster groups are compared to
see if it is the same samples which are most similar in both communities (Paavola et al. 2003;
Matson 2006). The ‗goodness of fit‘ of the classifications with each other is assessed using e.g.
ANalysis Of SIMilarities (ANOSIM) in Primer. The majority of recent studies have used
Mantel type tests to assess concordance between community similarity matrices (Heino 2010).
However, Gioria et al. (2011) recommend the use of a number of tests to increase confidence in
the assessment being carried out.
Studies of lentic communities have been largely carried out in North America and showed
promising results for concordance between fish and aquatic birds (Paszkowski & Tonn 2000)
and fish and macroinvertebrates (Jackson & Harvey 1993) in lakes. A more recent study was
carried out by Heino et al. (2009b) between groups of macroinvertebrates in boreal lakes in
Finland. That study found statistically significant relationships between the communities
studied but differing responses to environmental features. Bilton et al. (2006), in England
found high levels of concordance between chironomid, coleopteran, gastropod and trichopteran
communities of ponds. Gioria et al. (2011) examined the concordance between beetles and
macrophytes in ponds in two separate regions of Ireland, which suggested significant, but low
concordance between the two communities.
A large number of studies on concordance between taxonomic groups in running waters have
been carried out in Finland over the last decade. Some have dealt with the issue of within
community variation, focusing on cross-taxon congruence between macroinvertebrate groups
e.g. stoneflies, mayflies (Heino & Mykrä 2006; Mykrä et al. 2008b; Heino et al. 2009b), while
others have focused on between community concordance (Heino 2002; Paavola et al. 2003;
Heino et al. 2005; Paavola et al. 2006; Mykrä et al. 2008a), comparing macrophytes,
bryophytes, fish and macroinvertebrates. These studies have found concordance between
macrophytes, fish and three groups of macroinvertebrates (Heino 2002), poor concordance
between bryophytes, macroinvertebrates and fish in headwater streams (Paavola et al. 2003),
119
and significant concordance between bryophyte and macroinvertebrate diversity at those same
sites (Heino et al. 2005). The most recent conclusions from the Heino school (Heino 2010)
seem to suggest that levels of concordance between aquatic organisms may be too low for use
in conservation studies. A study of concordance between macrophytes and macroinvertebrates
in reference condition headwater streams in Ireland has also been carried out (Matson 2006)
and found significant concordance between the two communities. Dodkins et al. (2005b)
included the data of Matson in a study of concordance between macrophytes,
macroinvertebrates and phytobenthos and found good concordance between the three
communities. Again, all sites were of reference status.
Concordance between two communities may be affected by how long-lived they are (Dolph et
al. 2011), their relative mobility (Paszkowski & Tonn 2000; Paavola et al. 2003) and the
differing effects of season on the life cycles of the communities being studied (Birk et al. 2006;
Callanan et al. 2008). As some macrophytes may be more long lived than some of the
macroinvertebrate species recorded (Schaumburg et al. 2004), have less mobility than
macroinvertebrates, and are affected differently by season (Cotton et al. 2006; Sporka et al.
2006; Staniszewski et al. 2006) concordant responses of macrophytes and macroinvertebrates
may be subject to strong temporal and spatial variation (Rios & Bailey 2006; Mykrä et al.
2008b).
5.2 Aims
1. To assess within community concordance in the macrophyte community by assessing
concordance between bank macrophyte and aquatic taxa
2. To assess concordance between the macroinvertebrate and macrophyte communities,
using datasets of bank macrophyte taxa and aquatic taxa
3. To assess inter-seasonal and inter-annual variation in concordance between the
macrophyte and macroinvertebrate communities
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5.3 Methods
5.3.1 Biological Sampling
Macrophyte community composition and taxon abundance data were collected at the twenty six
sites shown in Figure 2.3. Data were collected using the methods outlined in Chapter 3.
Macroinvertebrate samples were collected in May 2006, October 2006 and May 2007,
following the guidelines of the EPA‘s Q-value system (Clabby et al. 2008). Twenty six sites
were sampled, coinciding with the sites selected for macrophyte surveys. Macroinvertebrates
were collected from riffles only, while macrophytes were collected over a 100m stretch. The
proportion of riffle varied from site to site and is given in Table 2.6. This discrepancy between
sampling areas may be a source of variation between the communities and results must be
interpreted accordingly. Sites were sampled by disturbing the substrate for 2 minutes using a
kicking action. Material disturbed by this kicking action was then collected in a handheld (1000
µm mesh) net. The collected material was placed into labelled plastic bags with 70 % Industrial
Methylated Spirits. Upon return to the laboratory, samples were clean sorted and specimens
identified to the levels required by the Q-value system. Data are presented in Appendix 4.
Samples were clean sorted to prevent any bias associated with subsampling (Haase et al. 2004).
Data were collated in the form of a total count for each taxonomic group per site. Data for the
sites coloured in green in Figure 2.2 were made available to the present study from the MSc
study of Niamh Sweeney (Sweeney 2011) and the contribution of her data to this study is duly
noted and appreciated. Training in the identification of macroinvertebrates was obtained
through attendance at a Field Studies Council course in Derrygonnelly, Co Fermanagh. After
each sampling period a subset of samples (3) were identified by both Niamh Sweeney and the
author for QA purposes. Any discrepancies highlighted by QA were resolved before further
identifications were carried out to increase accuracy and to decrease inter-surveyor variability.
Voucher specimens were also identified by Dr. Robert Cruikshanks (DkIT) and Pascal Sweeney
(Sweeney Environmental Consultancy).
121
5.3.2 Statistical Analysis
Concordance analysis was carried out in two separate ways, both using Primer v6 (Clarke &
Gorley 2006). Firstly, hierarchical cluster analyses were carried out on each community using
the CLUSTER routine, as in Chapter 3. The end groups of sites created by CLUSTER for each
community were compared to see if the relationships between sites were the same for each
community (Dodkins et al. 2005b). Only classifications which were found to be significant
using Primer‘s SIMPROF routine were compared. The level of agreement between these
classifications was then tested using ANOSIM, which is a non-parametric approximation of an
ANOVA test (Clarke & Warwick 2001). Where the structure of the two communities is similar,
the test statistic R will be close to 0. This analysis was followed by a Monte Carlo permutation
to assess the significance of the results. The end groups from each classification were colour
coded by Primer. These colours were presented on the classifications of the other community to
visualise the ‗goodness of fit‘.
The second method of analysis involved a newer type of community concordance analysis.
Community similarity matrices were compared using RELATE, a non-parametric, Mantel-type
test, which correlates similarity matrices element by element. Where the structure of the two
communities is similar, the test statistic ρ will approach 1. This analysis was followed by a
Monte Carlo permutation to assess the significance of the results.
Within community concordance was first tested by comparing the similarity in structure
between the Bank macrophyte and Aquatic macrophyte datasets used in Chapter 3. Between
community concordance was then tested by comparing the similarity in structure between both
the Bank macrophyte and Aquatic macrophyte datasets with the macroinvertebrate dataset.
Inter-seasonal and inter-annual variability in both within and between community concordance
was also explored by comparing the results of the concordance analyses for each of the
sampling periods.
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5.4 Results
5.4.1 Within Community Concordance
Hierarchical clustering analysis was carried out on both the Bank macrophyte and Aquatic
macrophyte datasets. No significant divisions were produced by clustering analysis for the
Aquatic macrophyte dataset for either each sampling period alone, or for a dataset of average
values over the three sampling periods. Composite datasets containing data from each of the
three sampling periods were then used as this produced dendrograms with significant divisions
(Figure 5.1). Figure 5.2 compares the classifications of both of the datasets i.e. the Bank
macrophyte dataset is colour coded with the Aquatic macrophyte classification (Figure 5.2a)
and the Aquatic macrophyte dataset is colour coded with the Bank macrophyte classification
(Figure 5.2b). This provides a visual representation of how well the two classifications agree.
The ANOSIM routine was then run to assess goodness of fit of the Aquatic macrophyte
classification on the Bank macrophyte dataset and vice versa. Both analyses showed
significant, but low levels of agreement between the two community classifications (r=0.587,
p=0.001 and r=0.203, p=0.001 respectively). The RELATE analysis also showed a significant,
but lower level of concordance between the two datasets (ρ=0.158, p=0.002) (Figure 5.2c).
123
a)
b)
Figure 5.1 Hierarchical cluster dendrograms for a) the combined Bank macrophyte dataset and b) the combined Aquatic macrophyte
dataset. Divisions of the dendrogram coloured in black are significant at the p<0.05 level. Coloured symbols show membership of
each cluster group
124
a)
b)
Figure 5.2. Hierarchical cluster dendrograms of a) the Bank macrophyte dataset colour coded with the Aquatic macrophyte
classification and b) the Aquatic macrophyte dataset colour coded with the Bank macrophyte classification. Divisions of the
dendrogram coloured in black are significant at the p<0.05 level. Coloured symbols show membership of each cluster group
125
5.4.2 Between Community Concordance
The macroinvertebrate taxa list for the Milltown lake catchment is presented in Table 5.1.
Macroinvertebrate count data is presented in Appendix 4.
Table 5.1 Macroinvertebrate taxa list with percentage frequency of occurrence across the
catchment in May 2006, October 2006 and May 2007
Order Taxon May 2006 October 2006 May 2007
Amphipoda Gammarus duebeni celticus Stock and Pinkster 87.5 96.2 84.6 Gammarus pulex L. 75 53.8 84.6 Bivalvia Pisidium spp. 8.3 3.8 7.7 Sphaerium spp. 20.8 26.9 15.4 Coleoptera Curculionidae 0.0 0.0 11.5 Dytiscidae 4.2 3.8 11.5 Elminthidae 91.7 96.2 96.2 Gyrinidae 0.0 11.5 15.4 Helodidiae 16.7 23.1 11.5 Diptera Chironomidae 95.8 96.2 100.0 Chironomus spp. 0.0 0.0 3.8 Eristalsis spp. 0.0 0.0 3.8 Simuliidae 33.3 88.5 42.3 Tipulidae 29.2 30.8 30.8 Ephemeroptera Baetidae 66.7 30.8 69.2 Baetis rhodani Pictet 95.8 96.2 96.2 Caenidae 33.3 23.1 38.5 Ecdyonourus spp. 54.2 61.5 61.5 Leptophlebiidae 0.0 0.0 3.8 Rhithrogena semicolorata Curtis 87.5 69.2 92.3 Seratella ignita Poda 12.5 0.0 7.7 Gastropoda Ancylus fluviatilis Muller 87.5 80.8 65.4 Planorbidae 0.0 11.5 3.8 Radix balthica L. 4.2 19.2 3.8 Hirudinea Hirudinea 29.2 84.6 46.2 Isopoda Asellidae 50.0 65.4 50.0 Megaloptera Sialidae 0.0 7.7 11.5 Oligochaeta Enchytraeidae 0.0 3.8 0.0 Lumbricidae 4.2 7.7 3.8 Lumbriculidae 4.2 0.0 7.7 Naidae 0.0 11.5 0.0 Tubificidae 8.3 7.7 15.4 Plecoptera Chloroperlidae 45.8 23.1 42.3 Leuctridae 45.8 26.9 53.8 Nemouridae 62.5 38.5 38.5 Perlidae 4.2 0.0 0.0 Perlodidae 70.8 26.9 57.7 Taeiniopterygidae 58.3 0.0 15.4 Glossosomatidae 70.8 80.8 76.9 Trichoptera Hydropsyche siltalai Döhler 66.7 53.8 53.8 Hydroptillidae 4.2 0.0 0.0 Lepidostomatidae 0.0 3.8 34.6 Leptoceridae 41.7 19.2 34.6 Limnephillidae 100.0 96.2 100.0 Odontocerum albicorne Spence 12.5 19.2 0.0 Philopotamidae 8.3 15.4 38.5 Polycentropodidae 66.7 57.7 61.5 Psychoididae 0.0 3.8 0.0 Ryacophillidae 29.2 42.3 53.8 Sericostoma personatum Spence 37.5 69.2 38.5
Hierarchical clustering analysis was carried out on the combined macroinvertebrate community
data (Figure 5.3). This analysis produced significant clusters, which were then compared,
firstly with the Bank macrophyte classification from Section 5.4.1 above, and secondly with
the Aquatic macrophyte classification.
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Figure 5.3 Hierarchical cluster dendrogram for the combined macroinvertebrate dataset. Divisions of the dendrogram coloured in
black are significant at the p<0.05 level. Coloured symbols show membership of each cluster group
127
5.4.2.1 Concordance Between the Bank Macrophyte Dataset and the Macroinvertebrate
Dataset
Figure 5.4 shows the dendrogram of the Bank macrophyte dataset colour coded with the
macroinvertebrate classification (Figure 5.4a) and the macroinvertebrate dataset colour coded
with the Bank macrophyte classification (Figure 5.4b). The ANOSIM routine was run to test
the goodness of fit of the macroinvertebrate classification on the Bank macrophyte dataset and
vice versa. Both analyses showed significant, but low levels of agreement between the two
community classifications (r=0.122, p=0.048 and r=0.276, p=0.001 respectively). The
RELATE analysis also showed significant, but poor concordance between the two datasets
(ρ=0.247, p=0.001).
5.4.2.2 Concordance Between Aquatic Macrophyte Dataset and the Macroinvertebrate
Dataset
Figure 5.5 shows the dendrogram of the Aquatic macrophyte dataset colour coded with the
macroinvertebrate classification (Figure 5.5a) and the macroinvertebrate dataset colour coded
with the Aquatic macrophyte classification (Figure 5.5b). The ANOSIM routine was run to test
the goodness of fit of the macroinvertebrate classification on the Aquatic macrophyte dataset
and vice versa. Both analyses showed significant, but low levels of agreement between the two
community classifications (r=0.116, p=0.021 and r=0.231, p=0.006 respectively). The
RELATE analysis did not show a significant relationship between the two datasets (ρ=0.034,
p=0.240).
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a)
b)
Figure 5.4 Hierarchical cluster dendrograms of a) the Bank macrophyte dataset colour coded with the macroinvertebrate classification
and b) the macroinvertebrate dataset colour coded with the Bank macrophyte classification. Coloured symbols show membership of
each cluster group
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a)
b)
Figure 5.5 Hierarchical cluster dendrograms of a) the Aquatic macrophyte dataset colour coded with the macroinvertebrate
classification and b) the macroinvertebrate dataset colour coded with the Aquatic macrophyte classification. Coloured symbols show
membership of each cluster group
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5.4.3 Temporal Variation in Community Concordance
5.4.3.1 Within Community Concordance
Hierarchical clustering analysis was carried out on the Bank macrophyte and Aquatic
macrophyte datasets from each of the sampling periods individually. The Bank macrophyte
dataset produced significant cluster groups for each of the sampling periods but the Aquatic
macrophyte dataset did not and so no cross comparison of classifications could be carried out.
The RELATE analyses was then carried out but did not produce significant results for any of
the sampling periods (Table 5.2).
Table 5.2 RELATE test statistics for Bank macrophyte v. Aquatic macrophyte datasets for each
sampling period
RELATE
ρ p
June 2006 ρ=0.064 p=0.263
September 2006 ρ=0.131 p=0.084
June 2007 ρ=0.115 p=0.131
5.4.3.2 Between Community Concordance
The macroinvertebrate data from the May 2006 period was compared to the macrophyte data
from the June 2006 period, the October 2006 data to the September 2006 data and the May
2007 data to the June 2007 data. The Bank macrophyte dataset and the macroinvertebrate
community dataset produced significant cluster groups for each of the sampling periods and so
comparison of classifications were carried out. The only sampling period for which the
ANOSIM routine showed significant similarity between the community and the test
classification was May/June 2006 (Table 5.3). This only held true for the use of the Bank
macrophyte classification on the macroinvertebrate community (r=0.324, p=0.035).
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Table 5.3 SIMPROF and ANOSIM test statistics for Bank macrophyte dataset v. the
macroinvertebrate community for each sampling period
Testing Macroinvertebrate
Classification on Bank Macrophyte
Dataset
Testing Bank Macrophyte
Classification on Macroinvertebrate
Community
SIMPROF ANOSIM SIMPROF ANOSIM
May/June
2006
Significant r=0.111
p=0.213
Significant r=0.324
p=0.035
Sept/Oct
2006
Significant r=-0.158
p=0.909
Significant r=0.166
p=0.094
May/June
2007
Significant r=0.066
p=0.276
Significant r=0.259
p=0.138
The RELATE analysis broadly agreed with the results of the comparison of classifications
(Table 5.4). Concordance between the two communities was not shown to be significant by the
RELATE analysis for any of the sampling periods.
Table 5.4 RELATE test statistics for the Bank macrophyte dataset v. the macroinvertebrate
community for each sampling period
RELATE
ρ p
May/June 2006 ρ=0.065 p=0.319
September/October 2006 ρ=0.082 p=0.220
May/June 2007 ρ=0.143 p=0.108
The analyses were then repeated, this time using the Aquatic macrophyte dataset and the
macroinvertebrate community data. As CLUSTER analysis did not produce significant
groupings for the Aquatic macrophyte dataset, classifications were not suitable to compare
with the macroinvertebrate community. The results of the RELATE analysis showed that
community concordance was highest between the two communities during September/October
2006 (ρ=0.357, p=0.001) (Table 5.5) Concordance between the two communities was not
shown to be significant by the RELATE analysis during the May/June 2006 period.
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Table 5.5 RELATE test statistics for the Aquatic macrophyte dataset v. the macroinvertebrate
community for each sampling period
RELATE
ρ p
May/June 2006 ρ=0.095 p=0.171
September/October 2006 ρ=0.322 p=0.001
May/June 2007 ρ=0.162 p=0.042
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5.5 Discussion
5.5.1 Within community concordance
Both bank and aquatic macrophyte species are subject to differing nature and degrees of
pressure from the water column and riparian zone (Osborne & Kovacic 1993; Kronvang et al.
2005) and so a difference in the structure of the two communities might be expected (Heino
2002; Bilton et al. 2006). Both the methods of concordance analysis used showed significant
concordance between the Bank macrophyte and Aquatic macrophyte datasets. However,
concordance was lower when the RELATE analysis was used. Heino (2010) suggests that an r
statistic of greater than 0.7 shows good agreement between two communities. As such, the
result of the ANOSIM test of the goodness of fit of the macrophyte classifications upon each
other suggests poor agreement. This poor concordance suggests that these communities are
either not responding to the same pressure or that they are not responding to that pressure in a
similar manner. This suggests that the Bank macrophyte classification is not robust enough to
provide information on structure in the Aquatic macrophyte dataset and vice versa. This further
suggests that these communities could not be used to predict or to explain the effects of
particular environmental gradients on each other.
Factors affecting concordance include spatial variation (Heino 2002; Paavola et al. 2006;
Dolph et al. 2011), temporal variation (Mykrä et al. 2008b), environmental conditions (Heino
2002; Mykrä et al. 2008a; Heino et al. 2009b; Gioria et al. 2011) and the methodology used
(Matson 2006; Gioria et al. 2011). There was no spatial or temporal variation indicated when
comparing the Bank and Aquatic macrophyte datasets in this study (same sites, same sampling
periods). However, the environmental conditions to which the two assemblages were subject to
may have been different. Physical pressures such as flow, which has been shown to explain
variation in macrophyte communities in streams (Chambers et al. 1991; Franklin et al. 2008)
would not impact the bank species and management practices such as cattle grazing and
fertiliser application in agriculture, which has been shown to affect riparian vegetation
(Kronvang et al. 2005) may not have affected the in-stream community in the same way (Rios
& Bailey 2006). These differing factors could have resulted in changes in each of the
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communities that would not necessarily have been mirrored by the other, thus decreasing
concordance.
Matson (2006), working on headwater sites across Ireland also found that the highest levels of
concordance in that study were within the macrophyte community. This relationship was,
however confined to the ability of a combined bank and aquatic dataset to explain the aquatic
dataset. In that study it was suggested that this was due to the paucity of aquatic species in
headwater streams and recommended that further study be undertaken in streams with greater
species richness. In contrast to the study of Matson (2006), the Aquatic dataset in the current
study showed greater power to explain variation in the Bank macrophyte dataset. This may be
due to the low diversity of the bank species recorded in the Milltown Lake catchment. Species
such as Juncus effusus L., Filipendula ulmaria L. Maxim, Angelica sylvestris L. and
Ranunculus flammula L. were recorded at the majority of sites sampled. This resulted in
greater homogeneity in the Bank macrophyte dataset and the creation of fewer cluster groups.
The disparity between species diversity on the banks of the reference headwater streams
studied by Matson and those in the current study may be due to the spread of the sites
surveyed. Sites surveyed by Matson were distributed throughout Ireland and were subject to
greater variation in geology, altitude and land use than the sites in the Milltown Lake
catchment. This variation may have increased the diversity of bank species encountered.
Although this may suggest the Aquatic macrophyte dataset has greater ability to explain the
variation in the bank macrophyte dataset, concordance was still too low to recommend that one
community might be able to act as a surrogate for another
5.5.2 Between Community Variation
There was some significant agreement between the macrophyte and macroinvertebrate
communities, however this was not strong (i.e. r>0.7 (Heino 2010)). Concordance between
communities was highest when the macrophyte classifications were imposed on the
macroinvertebrate classification. The highest of all was when the Bank macrophyte
classification was used (r=0.276, p=0.001). However, this level of concordance is still
considered low (Heino et al. 2009b). Although both tests have a scale of 0 to 1, with 1
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suggesting perfect concordance (Clarke & Warwick 2001), it is accepted that perfect
concordance between two different communities is unlikely (Heino & Mykrä 2006). The levels
of between community concordance found in the Milltown Lake catchment are similar to those
found by Heino et al. (2005) between macroinvertebrates, bryophytes and fish and by Mykrä et
al. (2008a) between macroinvertebrates and macrophytes.
Physico-chemical parameters which explained variation in the macrophyte community in the
Milltown Lake catchment included phosphorus, ammonium and Total Dissolved Solids (TDS)
(Chapter 3) and Sweeney (2011) found that BOD and conductivity explained the largest
amount of variation in the macroinvertebrate community of the Milltown Lake catchment.
Previous studies which have assessed the relationship between macrophytes and
macroinvertebrates and environmental variables have shown some agreement between the
factors shaping the two communities. Dodkins et al. (2005a) found conductivity, nitrate,
substrate, DO and pH to be significant in explaining variation in macrophyte communities in
Ireland and MacNeil et al. (2002) found that conductivity, nitrate and BOD were significant in
structuring macroinvertebrate communities in Northern Ireland.
TDS (and its correlate) conductivity can be affected by geology (EPA 2001) and landuse
(Donohue et al. 2006). Other authors have attributed the responses of biological communities
to conductivity to the relationship between conductivity, landuse and enrichment (Palmer et al.
1992; Dodkins et al. 2005a; Donohue et al. 2006; Feijoó & Lombardo 2007). Conductivity has
also shown negative relationships with water quality indices (Donohue et al. 2006; MacNeil et
al. 2002; Sweeney 2011). The relationship with conductivity might suggest that landuse
practices are impacting the water quality (Donohue et al. 2006). The effect of such practices on
instream nutrient concentrations may not have been captured by the seasonal sampling carried
out in this study under baseflow conditions. However, storm transfers can be responsible for a
large proportion of nutrient addition to streams and low flows can result in high nutrient
concentrations in streams (Donohue et al. 2005; Jordan et al. 2005; Arnscheidt et al. 2007).
The difference in the area sampled for each of the communities may also have affected the
number of species recorded in this study. If the macrophytes recorded only in the riffle area
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had been included, concordance may have been higher. However, the use of a smaller
macrophyte dataset may have impeded any statistical analysis of concordance. While the level
of agreement shown between the Bank macrophyte and macroinvertebrate datasets is higher
than that for the Aquatic dataset, it is so low that it does not suggest the two communities are
responding similarly, but independently to the same pressures (Paszkowski & Tonn 2000).
5.5.3 Temporal Variation in Community Concordance
Interpretation of the results of the analysis of the temporal variation in both within and between
community concordance in this study was hampered by methodological issues. The lack of
significant cluster groups in the individual sampling periods for the Aquatic dataset prevented
the cross testing of classifications and use of the RELATE routine. This is due to the low
number of aquatic species in the analysis (Clarke & Warwick 2001). This is a feature of
carrying out statistical analyses on aquatic communities where the low number of species
generally encountered (compared to terrestrial communities) may limit the use of certain
statistical tests (Cao et al. 2001; Demars & Harper 2005). The use of classification based
techniques such as RIVPACS (Wright et al. 1984) have been shown to be more robust where
there are at least 20 species (Marchant 2002). As the majority of sites sampled in Ireland by
Dodkins et al. (2005a), were found to have less than 20 species, classification based techniques
were abandoned in that study. The RELATE analysis, however is not limited by the need to
find significant multivariate structure within groups of sites as the CLUSTER-SIMPROF
method is. It only looks at whether the communities show a similar pattern in which site is
most similar to which, by matching among sample relationships in one similarity matrix to the
other (Clarke & Gorley 2006). This different approach allowed some comparison of the aquatic
dataset, but still did not result in significant relationships between the Bank and Aquatic
datasets. This discrepancy between analysis methodologies has been documented by other
authors (Matson 2006; Mykrä et al. 2008b; Heino et al. 2009b; Gioria et al. 2011) and
highlights a need for understanding of the methods being used. Although classification
analyses are useful in highlighting groups of similar sites for the purposes of conservation
(Palmer et al. 1992), they do not necessarily convey all of the underlying structure in the data,
as groups are compared rather than sites. The RELATE approach of correlating among sample
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relationships in one similarity matrix with the other, uses site by site information on whether
communities are showing similar patterns at sites. As such it may be better suited to analyses
of communities with low species richness.
Concordance between the Bank Macrophyte dataset and the macroinvertebrate dataset was
only significant during the May/June 2006 sampling period, and concordance between the
Aquatic Macrophyte dataset and the macroinvertebrate dataset was only significant during the
May/June 2006 sampling period. Although there was little temporal variation in the
macrophyte community (Chapter 3), there were changes in the macroinvertebrate community
that may have affected concordance between sampling periods. A shift between an
Ephemeroptera (Baetis rhodani Pictet and Rhithrogena semicolorata Curtis) dominated
community (43.6 %) in May 2006 and a Gammarus (Gammarus pulex L. and Gammarus
duebeni celticus Stock and Pinkster) dominated community in October 2006 (40.7 %) and May
2007 (33.2 %) was seen. This was due to an increase in numbers of G. pulex. G. d. celticus is
the most common Irish freshwater amphipod (Woods 1974). The invasive species G. pulex has
been introduced to the Republic of Ireland from Northern Ireland where it has displaced the
native G. d. celticus (MacNeil et al. 2004). G. pulex was present at 24 of the 26 sites sampled
and was the most abundant species collected from the catchment in May 2007. Its abundance is
highest in the sites from the TV tributary. The two sites from which G. pulex is absent are the
sites of the MA tributary, which drains Mullyash Mountain. This tributary is on the opposite
side of the catchment to the TV tributary. The ephemeropteran species, R. semicolorata was
the most common pollution sensitive macroinvertebrate recorded in the study and was found to
be one of the most common taxa across the catchment during May 2006, but numbers dropped
in October 2006. These figures were higher again in May 2007 since R. semicolorata has been
shown to be univoltine and to have overwintering larvae (Elliot et al. 1988). Its absence is
more usually attributed to pollution pressures (Clabby et al. 2008).
Statistical analysis of the differences in season could not be carried out as the levels of
concordance were not found to be significant. While low temporal variation in concordance
might be seen as desirable if communities are to be used as surrogates for one another, the low
levels of within community concordance found in that study meant that the authors did not
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recommend the community in question (macroinvertebrates of boreal headwater streams) for
use in conservation planning (Mykrä et al. 2008b).
5.6 Conclusions
5.6.1 To assess within community concordance in the macrophyte community by
assessing concordance between bank macrophyte and aquatic taxa
There was low within community concordance between the Bank macrophyte and Aquatic
macrophyte communities. This may suggest that these two communities were not responding
to the same pressure or that they were not responding in a similar way to that pressure. This
suggests that the Bank macrophyte classification is not robust enough to provide information
on structure in the Aquatic macrophyte dataset and vice versa. This further suggests that these
communities could not be used to predict or to explain the effects of particular environmental
gradients on each other and that the addition of the Bank macrophytes to water quality tools
such as CBAS may not increase the explanatory power of those tools.
5.6.2 To assess concordance between the macroinvertebrate and macrophyte
communities, using datasets of bank macrophyte taxa and aquatic taxa
There was low between community concordance between both of the macrophyte datasets and
the macroinvertebrate dataset. Concordance was higher between the Bank macrophyte dataset
and the macroinvertebrate dataset. However, the low levels of concordance found in these
analyses suggest that these two communities were not responding to the same pressure or that
they were not responding in a similar way to that pressure. This suggests that the macrophyte
classifications were not robust enough to provide information on structure in the
macroinvertebrate dataset and vice versa. This further suggests that these communities could
not be used to predict or to explain the effects of particular environmental gradients on each
other and could not act as surrogates for another in water quality and conservation assessments.
Factors such as the impact of the invasive amphipod G. pulex may have structured the
macroinvertebrate community to a greater extent than the environmental conditions. The
difference in the area sampled for each of the communities may also have impacted
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concordance. However, the use of a smaller dataset of riffle macrophytes to deal with this issue
may have impeded any statistical analysis of concordance due to the low species numbers
involved.
5.6.3 To assess inter-seasonal and inter-annual variation in concordance between
the macrophyte and macroinvertebrate communities
Seasonal variation in concordance could not be assessed statistically due to the lack of
significant results for each of the communities in each of the sampling periods. However,
between community concordance did vary between seasons. This may have been related to the
impact of the invasive amphipod G. pulex on the macroinvertebrate community. The
requirements of the classification analysis were too stringent to make this a useful approach for
the Aquatic macrophyte dataset due to the low number of species recorded. In such cases the
use of a more simple method, such as the RELATE routine, is recommended. The difference in
the area sampled for each of the communities may have impacted the number of species
recorded. However, the use of a smaller macrophyte dataset may have impeded any statistical
analysis of concordance due to the low species numbers involved.
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Chapter 6 Performance of Selected Biological Water Quality Indicators
in the Milltown Lake Catchment
_________________________________________________________________
6.1 Introduction
The number of river sites sampled by the Environmental Protection Agency (EPA) for water
quality monitoring is limited by economic constraints. As a consequence of this limitation there
is no possibility of their carrying out detailed sub-catchment studies. Although larger
monitoring networks are appropriate for long term trend analysis, these sampling regimes may
miss the causal relationships between local practices and poor water quality (Belle & Hughes
1983; Buck et al. 2004). The limited number of sites sampled for the national monitoring
program affects not only the number but also the type of site selected. Smaller first and second
order streams are neglected in favour of larger downstream sites. As a result, many of the
biological indices in use may not have been trialled at these stream types. Sites in the national
monitoring network are selected to represent the whole river system, but small first and second
order streams constitute as much as half of the country‘s river network. As WRBD (2005)
caution, if the risk of water pollution is proportional to the length of the river network then
these small streams may provide a conduit whereby half the discharges to rivers occur. This
may be of particular importance in the drumlin belt. This area is comprised of many small hills
with poorly drained alluvial gleys, peaty gleys and inter-drumlin peats with extensive blanket
bog at elevations above 150 m (Zhou et al. 2000). This creates an intricate drainage pattern of
small first and second order streams and brooks feeding the many lakes that lie in the
depressions between hills. Drainage improvements during periods of agricultural intensification
mean that flashy storm flows and suppressed base flows now characterise the hydrology of the
region (Douglas et al. 2007). The impacts of this complex hydrology (due to multiple
tributaries, non-uniform flow direction and gradient) has been investigated with regard to
chemical parameters of water quality and has been shown to influence phosphorus (P) flux from
septic tanks to water (Arnscheidt et al. 2007). High rural population intensity, intensive
agriculture and predominance of impermeable soil ensure that catchment typologies in this area
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conform to neither the traditional agricultural model of diffuse P sources, nor the traditional
urban model of point P sources (Arnscheidt et al. 2007). However, the spatial and temporal
variations in biological water quality indices over such a variable hydrological regime have not
been investigated.
Biological water quality indices have been shown to vary with season (Zamora-Muñoz et al.
1995; Brabec & Szoszkiewicz 2006; Sporka et al. 2006; Callanan et al. 2008). This has been
attributed to life cyles in the species on which these indices are based and changes in
temperature and food availability (Williams & Hynes 1973). However, the physico-chemical
quality of water has also been shown to vary with season (Neill 1989; Arnscheidt et al. 2007)
and the impacts of this have also been reflected in, for example, the macroinvertebrate
community (Morais et al. 2004). Disentangling the effects of decreases in water quality from
seasonal effects on biological communities may be difficult. Callanan et al. (2008) compared
seasonal differences in water quality indices based on macroinvertebrates in unimpacted
headwater streams in Ireland and found significant seasonal fluctuations due to the absence of
pollution sensitive taxa. Zamora-Muñoz et al. (1995) compared water quality indices along a
gradient of pollution in Spain and found that although the indices calculated were more related
to pollution than temperature, there were significant seasonal fluctuations. Šporka et al. (2006),
working on streams in Central Europe, compared water quality indices based on
macroinvertebrates in sites within a 100 m length to exclude the impacts of water quality and
also recorded seasonal fluctuations in index scores. Both Callanan et al. (2008) and Šporka et
al. (2006) found that sampling in summer resulted in the highest levels of fluctuations in index
scores and both recommended sampling in the spring to eliminate this.
The performance of both macrophyte and macroinvertebrate water quality indices have been
compared in the literature for a number of different purposes. These include selection of a
water quality assessment method (e.g. MacNeil et al. 2002; Thiébaut et al. 2006), testing a
particular water quality assessment method in a new region (e.g. Iliopoulou-Georgudaki et al.
2003) and for intercalibration of methods for the Water Framework Directive (WFD)
(Szoszkiewicz et al. 2006). However, caveats should be provided where indices are based on
different biological elements sampled over different areas e.g. diatoms and macrophytes as
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variation in both communities may result if sites are not physically homogenous. Comparisons
of the performance of indices in the literature has been carried out by both bivariate
(Iliopoulou-Georgudaki et al. 2003; Parr & Mason 2003; Azrina et al. 2006; Thiébaut et al.
2006) and multivariate (Rios & Bailey 2006; Szoszkiewicz et al. 2006; Feld & Hering 2007)
correlation analyses. Analyses have been carried out between the indices and measured
physico-chemical parameters and with each other (Birk et al. 2006; Schneider 2007).
Descriptive, site by site comparisons have also been carried out, without the use of statistical
analyses (Jarvie et al. 2002; Oťaheľová et al. 2007).
Jarvie et al. (2002) found that the Mean Trophic Rank (MTR) (Holmes et al. 1999) showed up
and downstream responses to P enrichment at a wastewater treatment plant in England, but that
a water quality index based on diatom communities did not. Iliopoulou-Georgudaki et al.
(2003) working in Greece compared indices based on macroinvertebrates, diatoms, fish and
macrophytes and found that the indices based on the macroinvertebrate community were the
most reliable. Thiebaut (2006) used both macroinvertebrate and macrophyte community
diversity and macroinvertebrate water quality indices and found that the macrophyte diversity
indices used did not adequately describe the water quality in the Moselle, France. Poor
agreement between the macrophyte indices and the physico-chemical parameters of the water
column resulted in the macroinvertebrate indices being recommended.
Correlation analyses of water quality indices with physico-chemical parameters, measured by
Azrina et al. (2006) in Malaysia, showed that the macroinvertebrate indices were most
correlated with measurements of total suspended solids (TSS) and conductivity. Parr & Mason
(2003), working in England, showed that the Lincoln Quality Index for macroinvertebrates was
significantly correlated with dissolved oxygen (DO), ammonia, total phosphorus (TP), total
oxidised nitrogen (TON), biochemical oxygen demand (BOD), soluble reactive phosphorus
(SRP), chloride and flow. However, each correlation only held for between one and eight of the
forty nine sites surveyed. Czerniawska-Kusza (2005) found that both diversity and water
quality indices based on macroinvertebrates showed significant correlations with a suite of
physico-chemical parameters measured in Poland and Otahelova et al. (2007) reported that
landuse was the most pertinent factor driving macroinvertebrate diversity indices in Slovakia.
143
Demars & Edwards (2009) found that the MTR was significantly correlated with inorganic
nitrogen (N) and phosphorus (P) in rivers in Scotland. This was echoed by Triest (2006) who
found that the MTR provided the most reliable water quality information at the sites sampled in
Belgian headwaters. Szoszkiewicz et al. (2006), working on sites from 14 European countries,
found that macrophyte water quality indices responded along a gradient of organic pollution in
lowland waters, at upland sites the relationship was less clear and was affected by site physical
characteristics too.
6.2 Aims
1. To assess water quality in small first and second order streams in a drumlin catchment
using macroinvertebrate and macrophyte indices commonly in use in Britain and
Ireland
2. To assess inter-seasonal and inter-annual variability in the water quality indices used
3. To compare the results of water quality assessments of the Milltown Lake Catchment as
determined by the macrophyte and macroinvertebrate indices
4. To examine correlations between the various indices and the measured physico-
chemical parameters of the water
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6.3 Methods
6.3.1 Macrophyte Sampling and Analysis Methodology
Macrophyte abundance data were collected at the twenty six sites shown in Figure 2.2.
Macrophyte data were collected using the methods outlined in Chapter 3. The abundance
classes were recorded on the MTR Scale A (Holmes et al. 1999), which is a nine point scale
(Table 6.1). Categorised abundance data was used to calculate the MTR score. All surveys and
the resulting scores were assessed using three measures of confidence outlined in the MTR
manual (Holmes et al. 1999). The first measure of confidence assessed survey
conditions/typicality at the site with regard to any interference from recent management
activities (e.g. weed cutting) or poor survey conditions (e.g. high turbidity). Surveys were
assessed on a scale of A-C based on how much of the survey data might have been affected:
· A - data not affected or any effect limited to less than 25% of the site
· B - the accuracy of records in 25–50% of the site influenced to a considerable degree
· C - the accuracy of records in >50% of the site influenced to a considerable degree
The MTR method states that surveys with a suffix of confidence of C should not be used for
comparison as the data may not be reliable. All sites in all sampling periods reflected the
prevailing conditions at the site and so a Suffix of Confidence of A was given to each survey.
The second measure of confidence required sites to be compared in a pairwise manner to
ensure that they were physically similar and suitable for comparison of MTR scores. The
factors under consideration for comparison were width, depth, substrate, habitats, shading,
water clarity and bed stability. Sites were assessed on a scale of I-III based on how much of the
site was similar:
· If 5 or more of these characteristics are similar for more than 75% of the site for each pair
of survey lengths, a category of I is assigned.
· If 3 or 4 of these characteristics are similar for more than 75% of the site for each pair of
survey lengths, a category of II is assigned.
· If 2 or less of these characteristics are similar for more than 75% of the site for each pair
of survey lengths, a category of III is assigned.
145
Site physical characteristics were recorded as in Chapter 2. Methods for recording substrate
and habitat types differed from the MTR methodology. Substrate types were ranked in order of
dominance instead of percentage cover being recorded. Sites were then compared based on the
most dominant substrate type. Sites were classed as similar if the substrate type ranked 1 at
both sites was the same. Flow types used the definitions in Chapter 2 for pool, riffle and glide,
rather than the MTR definitions for the habitat types pool, riffle, run and slack. Although this
constituted a deviation from the methodology, the data collected still provided information on
physical characteristics at the sites and so, was included in the site comparisons.
The impacts of these deviations from the method might be most important in the case of
substrate where the substrate type ranked 1 might not cover the 75 % of the site required for
comparability. To ensure that this did not unduly affect the comparability of sites, sites were
also compared without using substrate as a characteristic. This made it more difficult to
achieve the necessary level of comparability (II) as fewer characteristics could be used. Using
the substrate criteria the characteristics of the sites D1 and TV7 resulted in a large number of
comparisons being given a suffix of similarity of III (Appendix 1). MTR scores should not be
compared between sites which have a suffix of similarity of III. For this reason it was decided
to omit these sites from further analyses using the MTR. At least three physical characteristics
were similar between all other sites (except MA2 v. MA4), allowing them to be compared with
a Suffix of Comparability of I or II (Appendix 1). When substrate was not used to compare
sites there was only one additional pairwise comparison that dropped to a suffix of III (GO1 v
CN1). As these sites were comparable with the majority of other sites they were retained in the
analyses, but direct comparisons between their MTR scores may be dubious.
The third measure of confidence related to the number of reliable indicator taxa recorded at a
site. These taxa are highlighted in the MTR species checklist.
· a - > 8 highlighted (bold) taxa are present
· b - 5–8 highlighted (bold) taxa are present
· c - < 5 highlighted (bold) taxa are present.
Suffices of Confidence in the MTR scores are given in Appendix 1. The majority of MTR
scores had a Suffix of Confidence of c. A suffix of confidence in the MTR score of c does not
146
suggest the score is inaccurate, but greater confidence may be associated with scores with a
suffix of a or b (Dawson et al. 1999).
Species presence/absence data was used to calculate the Canonically Based correspondence
Analysis Score (CBAS) (NSSHARE 2008). Although CBAS does not currently use abundance
data, its collection is recommended for future method development. A six point scale is
suggested and can be easily compiled from any MTR data (Table 6.1). The calculation of
CBAS required additional information on the slope, alkalinity and width of the sampling sites.
The methodology for assessing alkalinity concentrations are as outlined in Chapter 3. Slope
and width for each site are presented in Table 2.6. Summary information for the macrophyte
water quality indices is given in Table 6.2.
Table 6.1 Abundance classes for macrophyte percentage cover values
MTR Abundance Classes CBAS Abundance Classes
<0.1% <0.1%
0.1-1% 0.1-1%
1-2.5% 1-2.5%
2.5-5% 2.5-5%
5-10% 5-10%
10-25% >10%
25-50%
50-75%
>75%
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Table 6.2 Summary information on the macrophyte water quality indices
Index MTR CBAS
Water quality
categories
>65 – Unlikely to be eutrophic
25-65 – At risk of becoming eutrophic, or
likely to be impacted by eutrophication.
<25 – badly damaged
*
≥0.9 – High
0.8-0.89 – Good
0.7-0.79 – Moderate
0.5-0.69 – Poor
<0.5 – Bad
Level of taxonomic
expertise required
High, species level ID required in field
High, species level ID
required in field
Recommended time of
use
June – September
June – September
Countries of use Britain, Ireland, Europe Northern Ireland,
Ireland
* Type specific
6.3.2 Macroinvertebrate Sampling and Analysis Methodology
Macroinvertebrate community count data were collected at the twenty six sites shown in
Figure 2.2. Macroinvertebrate data were collected using the methods outlined in Chapter 5.
Macroinvertebrate count data were then used to calculate the Q-value (Clabby et al. 2008) and
the Small Streams Risk Score (SSRS) (WRBD 2005). Macroinvertebrate presence/absence
data were used to calculate the British Monitoring Working Party score (BMWP) and the
Average Score Per Taxon (ASPT) (Armitage et al. 1983). Summary information for the
macroinvertebrate water quality indices is given in Table 6.3. As the BMWP and ASPT must
be interpreted with regard to river type, general water quality categories are not in use. The
current tool for assigning type specific scores using these indices (River Invertebrate
Classification Tool (WFD UKTAG 2008b)) has not been developed for the Irish typologies
and so those bandings were not used. Training courses in the use of the SSRS were provided
by the Aquens Consultancy. Accuracy of Q-value assignments was checked by cross reference
with the Environmental Protection Agency (EPA) monitored site in the catchment and through
confirmation of a subset of scores by Dr. Catherine Bradley (EPA). Accuracy of SSRS
assignments were checked by Dr. Maria Callanan, Aquens Consultancy.
148
Table 6.3 Summary information on the macroinvertebrate indices
Index Q-value SSRS BMWP ASPT
Water quality
categories
Q5 – Unpolluted
Q4 – Unpolluted
Q3 – Moderately
polluted
Q2 – Seriously
polluted
Q1 – Seriously
polluted
>8 – Probably
not at risk
6.5-8 – Probably
at risk
<6.5 – At risk
Higher scores
suggest better
water quality,
type specific
Higher scores
suggest better
water quality,
type specific
Level of
taxonomic
expertise
required
Few species
level IDs
required, can be
carried out on
bank with
experience
Designed for
bankside use
with only those
taxa that can be
IDed in the field
scoring
Family level IDs
required, can be
carried out on
bank with
experience
Family level IDs
required, can be
carried out on
bank with
experience
Recommended
time of use
May – October NA NA NA
Countries of
use
Ireland Ireland Britain, Ireland,
Europe, USA
Britain, Ireland,
Europe, USA
6.3.3 Chemical Sampling and Analysis Methodology
Water samples were taken at each of the sampling sites (except D1) on a monthly basis from
May 2006 as part of the larger National Source Protection Pilot Project and analysed as in
Chapter 3. Data are presented in Appendix 2. To aid interpretation of water quality index
scores, data were used to calculate annual median concentrations. These were based on a
median of 10 samples from the year May 2006 to May 2007 (except BOD where only 6 values
were available and for alkalinity where only 3 were available). This is in accordance with
current EPA methods for P and Total Ammonia (Martin McGarrigle, pers. comm.). These
concentrations could then be used to see if the site had failed the Environmental Quality
Standard (EQS), set out in the Surface Water Regulations (Minister for the Environment,
Heritage and Local Government 2009).
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6.3.4 Statistical Analysis
6.3.4.1 Inter-annual and inter-seasonal variation in water quality
Inter-annual and inter-seasonal variation in the water quality index scores, were assessed using
the non-parametric Kruskal Wallis test. The hypothesis tested was as laid out below:
H0: There is no difference in the water quality index results between sampling periods
H1: Water quality index results are different in at least one of the sampling periods
Pairwise Mann Whitney tests were then carried out to test for differences between results for
individual sampling periods. The hypothesis tested was as laid out below:
H0: There is no difference in the water quality index results between the two sampling periods
tested
H1: Water quality index results are different between the two sampling periods tested
6.3.4.2 Comparison of Water Quality Index Results
Comparison of water quality index results was carried out to assess the performance of the 2
macrophyte indices against the 4 macroinvertebrate indices. While surveys of both
communities were carried out at the same sites, the area from which the data were collected for
each community varied. This was due to the requirement that only riffles be sampled for the Q-
value system and the SSRS. It should be noted that differences between physical characteristics
along the sites could have impacted the responses of the communities.
Agreement between indices was assessed using two criteria. Firstly indices were assessed to
see whether they ranked the sites in the same order and secondly, whether or not the indices
assigned sites to the same water quality class. Where there is good agreement between the
indices both criteria should be satisfied. The MTR, BMWP and ASPT scores must be
interpreted with regard to river type. This was important in the Milltown Lake catchment as
variation in slope at the sites indicated different river types (Table 2.6).
150
1. To assess the relationship between the rankings provided by the indices, correlation analysis
was carried out. Where one index increased in score, an increase in the score of the others was
expected if they ranked the sites in the same order. Partial correlation analyses were carried out
on index scores from May/June 2007. Partial correlation was used to correct for the effects of
slope at the sites.
2. To facilitate a comparison of indices with differing numbers of water quality classes and to
provide a visual of water quality across the catchment using the different water quality indices
over the three seasons, a simple traffic light system was devised (Table 6.4). This system was
chosen in the absence of EU intercalibrated Ecological Quality Intervals (EQIs) for all
boundary classes for all indices. The water quality classes for each of the indices were assigned
to one of two colour coded categories. Red showed the most impacted sites in the catchment
and green showed sites that are least impacted. It should be noted that the system used provides
a gradient from most impacted sites in the catchment to least impacted sites, rather than
absolute values suggesting ‗high‘ or ‗bad‘ water quality. However, this boundary between the
green and red categories may approximate the good/moderate boundary at which decisions as
to programs of measures will be made under the WFD. As such this may be the most important
boundary at which to test agreement between indices. Where possible, the bandings selected
are based on the guides to the interpretation of results provided by each of the indices.
However, bands had to be refined for use in the Milltown Lake catchment for the MTR,
BMWP and ASPT as the scores for these indices are type specific.
MTR scores at reference condition sites for each of the river types in the catchment were
sourced from Kelly-Quinn et al. (2005) (Table 6.4). These scores were used to assign standard
descriptors to each of the sites based on their MTR scores. This was achieved through use of
the decision tree provided with the MTR manual (Figure 6.1). Where scores needed to be
compared to those at sites of a similar physical type, the ranges provided by Kelly-Quinn et al.
(2005) were used to assess if the scores were within the range expected for a reference
condition site (Table 6.4). Scores were then compared with the highest score achieved for that
river type within the current study. The highest scores was considered to show the best case
scenario MTR score for that river type within the catchment. Sites with lower scores were seen
151
to be limited by some factor not related to river type or physical characteristics. If physically
and typologically similar sites had different scores, the site with the lower score was seen to be
impacted as the macrophyte community was limited, resulting in lower MTR scores. Sites were
deemed have scores lower than expected for the river type when they were below the range set
out in Table 6.4 or less than the highest score for that river type in the catchment. Where other
evidence of eutrophication was required concentrations of TRP were assessed to see if they
were in excess of the EQS (0.035 mg/L P). Standard descriptors were then used in the traffic
light system as these can be compared like for like across river types (Holmes et al. 1999).
Table 6.4 River typologies for sites on the Milltown Lake catchment and ranges of MTR scores
at reference conditions for those river types (Source: Kelly-Quinn et al. 2005).
River type 21 22 23 24
Alkalinity 35 – 100 mg/L CaCO3
Slope < 5
m/km
5 – 20 m/km 20 – 40
m/km
> 40 m/km
River type to which
sites in Milltown
Lake were assigned
GO6 D2, TV1, TV3, TV5, TV6, GO1,
GO2, GO5, TH1, TH2, TH4, TH5,
TH6, MA4, DG1, DG2, CN1
TH3 TV2, TV4,
GO3, GO4
Range of MTR
Scores at reference
conditions (Kelly-
Quinn et al. 2005)
55 – 73
(9
sites)
51 – 81
(12 sites)
58 – 70
(4 sites)
65 (1 site)
152
Figure 6.1 Decision tree for the interpretation of MTR scores. Source: Holmes et al. 1999
For the BMWP and ASPT scores the water quality categories used were derived from other
studies which applied the indices in Ireland (Walsh 2005; Callanan et al. 2008). These
categories were then refined by correlation with the raw scores with different combinations of
the categories to obtain better agreement (Demars & Harper 1998). This was to ensure that the
banding used reflected the relationships between the scores at different sites. These bandings
must be interpreted with the understanding that absolute values are not being proposed for
interpretation of scores outside of the current study, merely that the index scores suggest that
sites in the red category are likely to be of poorer water quality than sites in the green category.
However, such simple systems allow comparison where a common metric does not exist and
have been shown to facilitate a fair and easily understandable assessment (Seele et al. 2000).
153
Table 6.4 Traffic light system for comparison of water quality indices within the Milltown
Lake catchment
MTR CBAS Q-value SSRS BMWP ASPT
1, 2 – not unduly
impacted by
eutrophication
≥0.9 – High
0.8-0.89 –
Good
Q5 –
Unpolluted
Q4 –
Unpolluted
>8 – Probably
not at risk
>101 >6.5
3 – may be at risk of
becoming eutrophic
4 – eutrophic or at risk of
becoming eutrophic
0.7-0.79 –
Moderate
0.5-0.69 –
Poor
<0.5 – Bad
Q3-4 Slightly
polluted
Q3, Q2-3
Moderately
polluted
6.5-8 –
Probably at
risk
<6.5 – At risk
<100
<6.5
6.3.4.3 Examination of the relationship between water quality index scores and the
physico-chemical parameters
Water quality index scores and the physico-chemical parameters of the water were compared
by correlation analysis. Kolmogorov-Smirnov tests showed that log(x) transformed physico-
chemical parameters were not significantly different from the normal distribution and so data
were compared using Pearson‘s correlation coefficient. Correlations were carried out between
the May/June 2007 water quality index scores and the dataset of median values from samples
taken over the year from May 2006 to May 2007 for each site. Partial correlation was used to
correct for the effects of slope at the sites.
MTR scores may also be interpreted with regard to up and downstream changes in water
quality. Assessment of the MTR has shown that a change in score of more than 4 MTR units
between two sites represents a significant change in water quality, taking survey and surveyor
error into account (Holmes et al. 1999). For this reason significant up and downstream changes
in water quality between sites which were physically similar and of the same river type were
highlighted. Up and downstream changes in scores were assessed with regard to coincident
changes in the physical and physico-chemical characteristics of the sites. This was carried out
by mapping the up and downstream changes and comparing conditions between sites. Again
the MTR scores from June 2007 were used as these could be compared to the dataset of annual
median physico-chemical characteristics.
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6.4 Results
6.4.1 Water Quality in the Milltown Lake Catchment
A summary guide to the interpretation of the macrophyte index scores can be found in Table
6.2. The calculated macrophyte index values are given in Table 6.5. In June 2006 two sites
(TV6 and GO4) were not sampled due to lack of macrophyte growth at these sites and in June
2007 index scores were not calculated as only two species were recorded (MA2). A summary
guide to the interpretation of the macroinvertebrate index scores can be found in Table 6.3. The
calculated macroinvertebrate index values are given in Table 6.5. The samples collected from
TH5 and TV7 in May 2006 were not included in the analysis as high levels of organic matter in
the sample interfered with the preservation and identification of the samples.
6.4.1.1 May/June 2006
6.4.1.1.1 Macrophytes
The sites TV4, GO3 and MA4 were assigned standard descriptors of 1 and were ‗not unduly
impacted by eutrophicaton‘ (Table 6.5). The site GO6 was assigned a standard descriptor of 6.
This meant that the MTR alone could not be used to assess the impacts of eutrophication and
that the site exceeded the TRP EQS for the good/moderate boundary for rivers (0.035 mg/L P).
The rest of the sites were assigned a standard descriptor of 4 as their scores were less than
might be expected for their physical type. This suggested that these sites were ‗eutrophic or at
risk of becoming eutrophic‘. The site with the highest MTR score was GO3 (70) and the site
with the lowest MTR score was TV2 (36.6). In general sites with the highest MTR scores were
located mid tributary (TV4, GO3, TH5, MA4), while sites with the lowest scores were located
nearer the source or on the smaller tributaries TV1, TH1, MA2, DG2). In the CBAS analysis of
the macrophyte community in June 2006, one site was found to be of ‗high‘ status (Table 6.5),
seven sites were classed as ‗good‘, seven sites were classed as ‗moderate‘, seven sites were
classed as ‗poor‘ and two sites were classed as ‗bad‘. The ‗bad‘ status sites were GO5 (0.46)
and TH1 (0.36). TH1 also had a low MTR score (0.41). The site with the highest score (0.90)
was TV7, a site which the physico-chemical parameters suggest is impacted.
155
Table 6.5 Water quality indices scores for each site for each sampling period. Standard descriptors for the MTR scores are given after
the score
May/June 2006 September/October 2006 May/June 2007
Site MTR CBAS Q SSRS BMWP ASPT MTR CBAS Q SSRS BMWP ASPT MTR CBAS Q SSRS BMWP ASPT
D1 - 0.68 3-4 6.4 65 5.91 - 0.61 3-4 8 108 6.00 - 0.70 3-4 8.8 118 6.56
D2 58.3 0.88 3 7.2 50 6.25 56.7 0.76 3-4 5.6 105 6.18 58.0 0.79 3-4 8 115 6.76
TV1 41.6 0.82 2-3 1.6 37 4.63 43.8 0.76 2-3 3.2 46 4.60 42.5 0.77 2-3 2.4 40 4.44
TV2 36.6 0.67 4 6.4 75 6.25 36.7 0.67 3-4 3.2 69 5.75 40.0 0.67 3-4 5.6 65 5.91
TV3 57.3 0.82 3-4 4.8 53 5.89 56.9 0.74 3 1.6 60 4.29 63.6 0.96 3-4 5.6 77 5.13
TV4 66.7 0.89 3-4 8 80 5.71 65.4 0.88 3 6.4 57 5.70 65.5 0.97 3 5.6 55 5.50
TV5 62.8 0.76 3 5.6 30 5.00 61.2 0.66 3 4.8 52 4.73 65.0 0.85 3-4 7.2 89 6.36
TV6 - - 4-5 10.4 116 6.82 60.0 0.85 3 2.4 37 5.29 58.0 0.85 3 4.8 46 5.11
TV7 - 0.90 - - - - - 0.90 3 3.2 40 4.44 - 0.90 3-4 4.8 72 6.00
GO1 55.0 0.67 4 8.8 118 6.21 53.1 0.66 3-4 3.2 60 5.00 56.6 0.80 4 7.2 106 5.89
GO2 54.0 0.71 4-5 11.2 144 7.20 52.3 0.65 4-5 8 103 5.72 56.9 0.69 4-5 10.4 116 7.25
GO3 70.0 0.62 4 8.8 62 6.20 66.2 0.62 4-5 11.2 139 6.95 70.8 0.75 4-5 11.2 123 6.83
GO4 - - 4-5 11.2 139 7.32 63.3 0.89 4-5 8 119 6.61 68.0 0.89 4-5 9.6 123 7.69
GO5 55.4 0.46 4-5 10.4 118 6.94 55.0 0.46 3-4 6.4 90 6.00 58.0 0.61 4-5 11.2 113 7.06
GO6 56.9 0.84 3-4 9.6 138 7.26 56.4 0.78 3-4 7.2 108 6.00 56.4 0.78 4-5 7.2 96 6.40
TH1 41.0 0.36 3-4 6.4 90 5.63 42.3 0.36 2-3 4.8 81 5.79 43.6 0.36 4 7.2 93 5.81
TH2 53.6 0.71 4-5 10.4 136 7.55 55.0 0.71 4 10.4 117 6.50 55.3 0.71 4-5 8.8 103 6.87
TH3 58.7 0.78 4-5 10.4 153 7.65 57.8 0.78 4-5 11.2 132 6.95 63.7 0.92 4-5 10.4 113 7.06
TH4 48.0 0.65 4-5 8.8 109 7.27 46.2 0.51 4-5 11.2 132 6.60 50.0 0.88 4-5 10.4 130 7.22
TH5 60.0 0.77 - - - - 60.0 0.77 3-4 6.4 74 5.29 61.8 0.86 3-4 9.6 89 5.93
TH6 46.7 0.76 4 9.6 102 7.29 47.1 0.76 3 6.4 73 5.62 52.5 0.78 3-4 7.2 104 6.93
MA2 48.0 0.54 4 9.6 107 6.69 45.7 0.54 4 8.8 77 6.42 - - 4 8.8 105 7.00
MA4 69.4 0.66 4 8.8 131 6.55 67.6 0.72 4-5 8.8 157 6.83 71.3 0.87 4-5 8.8 139 6.95
DG1 56.7 0.85 4 9.6 90 6.43 55.7 0.82 4 8 98 6.13 55.4 0.84 4 8 114 6.71
DG2 42.0 0.79 4 8.8 110 6.88 42.0 0.79 3-4 8 78 6.50 40.0 0.80 4 8 70 6.36
CN1 52.9 0.87 4 7.2 120.5 6.69 53.0 0.87 3 4 90.1 6.01 54.3 0.86 3-4 4 111.2 6.18
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6.4.1.1.2 Macroinvertebrates
Sixteen of the sites sampled in May 2006 were classed as ‗unpolluted‘ by the Q-value system
(Table 6.5). Five of the sites were classed as ‗slightly polluted‘ (TV3, TV4, D1, TH1 and
GO6). Three sites achieved a class of Q3 (D2, TV5) or 2-3 (TV1), which is intermediate
between Q2 and Q3 and are termed ‗moderately polluted‘. One of the sites which was
‗moderately polluted‘ was located immediately downstream of the lake which feeds the stream
(TV1). The other two sites (TV5 and D2) which were classed as ‗moderately polluted‘ were at
downstream locations, the site D2 is located just above the inflow to Milltown Lake itself. The
sites with the highest Q-values were located on the middle of the TH and GO tributaries. Using
the SSRS, fifteen sites were placed in the ‗probably not at risk‘ category, three were placed in
the ‗probably at risk‘ category and six of the sites were placed in the ‗at risk‘ category (Table
6.5). The site which was shown to be at greatest risk was TV1 with a score of 1.6 and the site
which was shown to be at least risk were GO2 and GO4, both with a score of 11.2. The
majority of sites in the catchment (14) were classed as ‗good‘ by the BMWP. Much of the TV
tributary was shown to be impacted and only one of its sites was classed as ‗good‘ (TV6). The
site with the highest BMWP score was TH3 (159.7) and the lowest was TV5 (32.4). The
majority of sites in the catchment (13) in May 2006 achieved ASPT scores greater than 6.5,
suggesting that these sites are ‗good‘ (Table 6.5). The site with the highest ASPT was TH2
with a score of eight and the lowest was TV1 with a score of 4.63. The TH tributary exhibited
the highest water quality with the majority of sites scoring between seven and eight. The TV
tributary was the worst with scores of between 4.63 and 6.82.
157
6.4.1.2 September/October 2006
6.4.1.2.1 Macrophytes
Again, the sites TV4, GO3 and MA4 had MTR were assigned standard descriptors of 1 and
were ‗not unduly impacted by eutrophicaton‘ (Table 6.5). The site GO4 was assigned a
standard descriptor of 2 which suggests the site was ‗not unduly impacted by eutrophicaton‘.
The rest of the sites were assigned a standard descriptor of 4 as their scores were less than
might be expected for their physical type. This suggested that these sites were ‗eutrophic or at
risk of becoming eutrophic‘. The site with the highest MTR score was MA4 with a score of
67.6, this was only 1.4 MTR units higher than the site GO3, which was the highest scoring site
in June 2006. The site with the lowest score was again, TV2 (36.7). The results of the CBAS
calculations for September 2006 showed that one sites was classed as ‗high‘ status, five sites
were given ‗good‘ status, ten of the sites were of ‗moderate‘ status, eight sites were of ‗poor‘
status, and again, two sites were classed as ‗bad‘ (GO5 and TH1) (Table 6.5). TV7 was again
given the highest score (0.90) and TH1 again had the lowest score (0.36).
6.4.1.2.2 Macroinvertebrates
Overall quality of water in the catchment as shown by the Q-value system decreased from May
2006 to October 2006 (Table 6.5). Eleven sites decreased in Q-value from May 2006 to
October 2006 (MA4, GO3 and D2), only three sites improved in Q-value, ten sites remained
the same, the remaining two sites were assessed for the first time in October 2006. Nine sites
were classed as ‗unpolluted‘, eight sites were classed as ‗slightly polluted‘ and nine sites were
classed as ‗moderately polluted‘. The SSRS showed that five sites were ‗probably not at risk‘,
seven sites were classed as being ‗probably at risk‘ while the majority (14) of the sites sampled
were classed as ‗at risk‘. Three sites (GO3, TH3 and TH4) shared the highest SSRS, which was
11.2, the other sites which were ‗probably not at risk‘ were TH2 and MA2. The three TH sites
which achieved scores placing them in the ‗probably not at risk‘ category were all downstream
of one another, suggesting that that the TH tributary was of good quality. While the majority of
sites were found to be ‗at risk‘, the sites with the lowest SSRS (1.6) were TV3 and TV6, while
TV1 also had a low score of 2.4. This suggests that the TV tributary is of a poor standard. This
158
also suggests that something has impacted severely on the site TV6 as this site was classed as
‗probably not at risk‘ in May 2006 when it achieved a score of 10.4. BMWP scores also
decreased in October 2006 with ten sites classed as ‗good‘ (Table 6.5). The site with the
highest BMWP score was MA4 (168.8), the site with the lowest BMWP score was TV6 (38.1).
The number of sites classed as ‗good‘ by the ASPT decreased to seven in October 2006. The
sites with the highest scores for this season were GO3 and TH3, both scoring 6.95 and the site
with the lowest score for the season was TV3 with a score of 4.29. The TV tributary was again
the poorest in quality with four of the sites scoring less than five (TV1, TV3, TV5 and TV7).
6.4.1.3 May/June 2007
6.4.1.3.1 Macrophytes
The sites TV4, GO3, GO4 and MA4 had MTR scores of greater than 65, were assigned
standard descriptors of 1 and were ‗not unduly impacted by eutrophicaton‘ (Table 6.5). The site
GO4 improved in MTR score from 63.3 in September 2006 to 68 in June 2007. This represents
a significant increase in MTR score and there was no other evidence of eutrophication at the
site. The rest of the sites were assigned a standard descriptor of 4 as their scores were less than
might be expected for their physical type. This suggested that these sites were ‗eutrophic or at
risk of becoming eutrophic‘. Significant changes in MTR scores (difference of 4 units) were
observed between TV2 and TV3 and between TV5 and TV6, between GO2 and GO3 and
between GO4 and GO5 and between all of the sites of the TH tributary. TV2 and DG2 shared
the lowest MTR score (40), which represented an increase in score for TV2 and a decrease for
DG2. MA4 had the highest MTR score with a score of 71.3, while GO3 had a score of 70.8.
The results of the CBAS calculations for September 2006 showed that four sites were classed
as ‗high‘ status, ten sites were classed as ‗good‘, seven sites were classed as ‗moderate‘, three
sites were classed as ‗poor‘ and one site was classed as ‗bad‘ (TH1) (Table 6.5). The sites with
the highest EQRs were TV3 (0.96) and TV4 (0.97). The site with the lowest EQR was again,
TH1 with a score of 0.36.
159
6.4.1.3.2 Macroinvertebrates
The Q-value system showed an increase in water quality from October 2006 to May 2007,
eleven sites increased in Q-value and no site decreased in Q-value (Table 6.5). Thirteen sites
were classed as ‗unpolluted‘, nine sites were classed as ‗slightly polluted‘ and three sites were
classed as ‗moderately polluted‘. The SSRS showed that a larger proportion of the sites were
‗probably not at risk‘ (Table 6.5). The number of sites in the ‗probably at risk‘ category
increased only by one and so the improvement in the scores achieved was seen mostly in the
reduction in the number of ‗at risk‘ sites and the increase in the number of ‗probably not at
risk‘ sites. Seven sites were placed in the ‗at risk‘ category, eight sites were placed in the
‗probably at risk‘ category and eleven sites were classed as ‗probably not at risk‘. The sites
with the highest SSRS were GO3 and GO5, both of which achieved a score of 11.2, the site
with the lowest SSRS was the site TV1. The site TV5 was the only site on the TV tributary
which was not classed as ‗at risk‘, again highlighting the TV tributary as being of poor quality.
BMWP scores improved in May 2007 (Table 6.5). The majority of sites (15) were classed as
‗good‘ (Table 6.5). The site with the highest BMWP score was MA4 (148.5) and the site with
lowest BMWP score was TV1 (41.1). The ASPT classed half of the sites (13) as ‗good‘ (Table
6.5). The site with the highest score was GO4 with a score of 7.69 while the site TV1 had the
lowest score of 4.44.
160
6.4.2 Inter-Seasonal and Inter-Annual Variability
Boxplots of the data are presented to illustrate the temporal variation in the water quality
indices (Figure 6.2). The biological indices results were analysed to test for statistical
differences between the three seasons (May/June 2006, September/October 2006 and May/June
2007) (Table 6.7). Kruskal Wallis tests showed that there were significant differences between
the seasons as measured by CBAS (χ2=8.341, p=0.015), the SSRS (χ2=6.055, p=0.048) and the
ASPT (χ2=9.666, p=0.008). Pairwise Mann Whitney tests showed significant differences
between CBAS scores in September 2006 and June 2007 (U=188, p=0.010) and between
between June 2006 and June 2007 (U=189, p=0.016). There were significant differences
between the SSRS scores in May 2006 and October 2006 (U=191.5, p=0.019) and between the
ASPT scores between May 2006 and October 2006 (U=165.5, p=0.004) and between May
2006 and May 2007 (U=204.5, p=0.015) (Table 6.8).
Table 6.7 Kruskal Wallis χ2 test statistics for differences in water quality indices between
seasons. Asterisked values highlight significant results
MTR CBAS Q-Value SSRS BMWP ASPT
χ2 1.035 8.341 4.326 6.055 1.901 9.666
p 0.596 0.015* 0.115 0.048* 0.387 0.008*
Table 6.8 Mann Whitney U test statistics for the pairwise differences in water quality indices
between seasons. Asterisked values highlight significant results
May/June 06 v.
Sept/Oct. 06
Sept/Oct. 06 v.
May/June 07
May/June 06 v.
May/June 07
U p U p U p
MTR 285 0.451 284.5 0.327 319.5 0.917
CBAS 315 0.850 188 0.010* 189 0.016*
Q-value 219.5 0.065 247.5 0.086 301 0.824
SSRS 191.5 0.019* 254 0.122 257 0.282
BMWP 251 0.336 276 0.256 291 0.683
ASPT 165.5 0.004* 204.5 0.015* 284 0.587
161
Figure 6.2 Boxplots of water quality index scores by season
162
6.4.3 Inter-Comparison of Water Quality Indices
6.4.3.1 Comparison between Water Quality Index Scores
None of the indices showed perfect agreement with each other in ranking of sites. Table 6.9
shows the results of the correlation analyses between the water quality index scores. The MTR
and CBAS were significantly positively correlated with each other (r=0.489, p=0.021) (Table
6.9). The MTR was significantly positively correlated with the ASPT (r=0.452, p=0.035)
(Table 6.9). All of the macroinvertebrate indices were significantly positively correlated with
each other. The highest correlation coefficient returned by the analyses between the
macroinvertebrate indices was between the BMWP and the ASPT (r=0.867, p=0.000) and the
lowest was between the SSRS and the ASPT score (r=0.742, p=0.000).
Table 6.9 Partial correlation coefficients (r), p values and sample number (n) for water quality
indices for May/June 2007. Asterisked values highlight significant results
MTR CBAS Q-value SSRS BMWP
CBAS r 0.489
p 0.021*
n 23
Q-value r 0.281 -0.189
p 0.205 0.400
n 23 23
SSRS r 0.375 -0.129 0.838
p 0.086 0.568 0.000*
n 23 23 24
BMWP r 0.296 0.048 0.825 0.816
p 0.182 0.831 0.000* 0.000*
n 23 23 24 24
ASPT r 0.452 -0.019 0.797 0.742 0.867
p 0.035* 0.934 0.000* 0.000* 0.000*
n 23 23 24 24 24
163
6.4.3.2 Comparison between Water Quality Index Categories
The macrophyte indices used did not show good agreement based on the traffic light system.
The majority of sites in each season were shown to be of moderate quality or worse by the
MTR. CBAS showed that the majority of sites were moderate or worse in June and September
2006 and good or better in June 2007 (Table 6.10). Seasonal variation in macroinvertebrate
indices scores was evident in Table 6.10. The Q-value classed the majority of sites as green in
May 2006 (16), red in October 2006 (16) and green again in May 2007 (14). The SSRS
provided the poorest picture of water quality across the catchment in each of the three seasons
with sixteen sites in the red category in May 2006, twenty one in October 2006 and fifteen
again in May 2007. The BMWP classed the majority of sites as green in May 2006 (14), red in
October 2006 (14) and green again in May 2007 (15). The ASPT classed half the sites as green
in May 2006 (13) and red in October 2006 (19). In May 2007 it assigned half of the sites to the
green category and half of the sites to the red category.
The two macrophyte indices agreed on fourteen sites in June 2006 (TV2, TV4, TV5, GO1,
GO2, GO5, TH1 - TH6, MA2, DG2), nineteen sites in September 2006 (D2, TV1 – TV5, GO1,
GO2, GO4 - GO6, TH1 - TH6, MA2, DG2) and eleven in June 2007 (D2, TV1, TV2, GO2,
GO4, GO5, GO6, TH1, TH2, TH6, MA4) (Table 6.10). In the case of the macroinvertebrates,
there was better agreement between the indices used based on the traffic light system. The
macroinvertebrate indices agreed on seventeen sites in May 2006 (D1, D2, TV1, TV3, TV5,
TV6, GO2, GO4, GO5, TH1 - TH4, TH6, MA2, MA4 and DG2), seventeen sites in October
2006 (TV1 – TV7, GO1, GO3, GO5, TH1 – TH6 and MA4) and sixteen sites in May 2007
(TV1 - TV7, GO2 - GO5, TH2, TH3 - TH4, MA2 and MA4).
The only agreement between all of the indices was based on sites in the highest water quality
category in the traffic light system (green). All indices did not agree on assigning sites to either
the orange or red categories. There was no agreement between all indices in May/June 2006
(MA4) or September/October 2006 (GO3 and MA4). The only agreement between all indices
was for 2 sites in May/June 2007 (GO4 and MA4).
164
Table 6.10 Traffic light class assigned to each site for each sampling period based on the categories assigned to index scores in Table
6.4
May/June 2006 September/October 2006 May/June 2007
Site MTR CBAS Q SSRS BMWP ASPT MTR CBAS Q SSRS BMWP ASPT MTR CBAS Q SSRS BMWP ASPT
D1
D2
TV1
TV2
TV3
TV4
TV5
TV6
TV7
GO1
GO2
GO3
GO4
GO5
GO6 * *
TH1
TH2
TH3 *
TH4
TH5
TH6
MA2
MA4
DG1
DG2
CN1
* MTR score could not be used to assess eutrophication at the site as a standard descriptor of 6 was assigned
165
6.4.4 Comparison between Water Quality Indices and Physico-chemical
Parameters of the Water Column
Table 6.11 gives the summary statistics for the dataset of median physico-chemical parameters
of the water column for the year May 2006 to May 2007. Figure 6.1 plots the relationships
between the physico-chemical parameters and the water quality index scores. Table 6.12 shows
the results of the correlation analyses between the measured physico-chemical parameters and
the water quality index scores. The MTR was significantly negatively correlated with TRP
mg/L (r=-0.515, p=0.014). CBAS did not show significant correlation with any of the physico-
chemical parameters measured. All of the macroinvertebrate indices were significantly
negatively correlated with TRP (Table 6.12). The Q-value, the SSRS and the BMWP were
significantly negatively correlated with conductivity. The ASPT showed a relationship with
conductivity which was very close to significant at the 5 % level (r=-0.420, p=0.051). The Q-
value and the BMWP score were significantly negatively correlated with ammonium (r=-0.427,
p=0.048 and r=-0.451, p=0.035). The SSRS was significantly negatively correlated with
alkalinity (r=-0.447, p=0.037).
Table 6.11 Summary statistics for the dataset of median physico-chemical parameters of the
water column for the year May 2006 to May 2007
Cond.
µS/cm
pH DO
%SAT
BOD
mg/LO2
NH4
mg/L N
TRP
mg/L P
Alk.
mg/L CaCO3
Mean 157.37 7.54 85.21 2.10 0.080 0.060 58.23
Std. Err. 5.68 0.03 1.46 0.10 0.009 0.004 2.30
Median 151.10 7.51 85.95 2.09 0.070 0.060 54.00
Min. 106.4 7.19 65.8 1.19 0.02 0.03 42.00
Max. 219.5 7.85 95.2 3.28 0.18 0.13 84.00
N 25 25 25 25 25 25 26
166
Figure 6.1 Draftsman plot of relationships between the water quality indices for May/June 2007 and a median value for physico-
chemical parameters of the water column for the year May 2006 to May 2007
167
Table 6.12 Partial correlation coefficients (r), p values and degrees of freedom (df) for the water quality indices for May/June 2007
and a median value for physico-chemical parameters of the water column for the year May 2006 to May 2007. Asterisked values
highlight significant results, n=24
MTR CBAS Q SSRS BMWP ASPT
Conductivity µS/cm r -0.213 0.186 -0.642 -0.492 -0.511 -0.420
p 0.342 0.407 0.001* 0.020* 0.015* 0.051*
df 20 20 20 20 20 20
pH r 0.340 0.368 0.095 0.051 0.133 0.000
p 0.122 0.092 0.675 0.820 0.556 0.999
df 20 20 20 20 20 20
Alkalinity mg/L CaCO3 r 0.048 0.213 -0.361 -0.447 -0.359 -0.301
p 0.831 0.342 0.099 0.037* 0.101 0.174
df 20 20 20 20 20 20
DO % SAT r 0.388 0.372 0.228 0.386 0.355 0.209
p 0.074 0.088 0.307 0.076 0.104 0.351
df 20 20 20 20 20 20
BOD mg/L O2 r 0.015 -0.107 0.180 0.266 0.153 0.107
p 0.946 0.637 0.423 0.232 0.495 0.634
df 20 20 20 20 20 20
Ammonium mg/L N r -0.174 0.281 -0.427 -0.381 -0.451 -0.248
p 0.439 0.205 0.048* 0.080 0.035* 0.266
df 20 20 20 20 20 20
TRP mg/L P r -0.515 0.024 -0.561 -0.491 -0.592 -0.554
p 0.014* 0.915 0.007* 0.020* 0.004* 0.007*
df 20 20 20 20 20 20
168
6.4.4.1 Up and Downstream Changes in MTR Scores
Changes in MTR scores of greater than 4 units show significant impacts to water quality
between up and downstream sites. Up and downstream comparisons of MTR scores should not
be made between sites which are of a different river type. However, comparisons can be made
with other physically and typologically similar sites further downstream. Sites which showed a
downstream change of ±4 MTR units are highlighted in Figure 6.2. Pairs of sites are colour
coded to show which site is being compared with which. Pairs of sites between which there
were significant up and downstream differences and which could be compared without
variation due to river type or physical dissimilarity, are presented in Table 6.13. Sites up and
downstream of tributaries were included in the current study as the comparison might allow the
impact of that tributary to be assessed.
Significant differences in score were coincident with changes in TRP concentration between
DG1 and DG2, TH1 and TH2 and between TH2 and TH4 (Table 6.13). Between DG1 and
DG2 and between TH2 and TH4 there were increases in TRP which coincided with decreases
in the MTR score at the downstream site. Between TH1 and TH2 there was a decrease in TRP
which coincided with a downstream increase in the MTR score. This was not the case between
TV5 and TV6 and TH5 and TH6 where TRP concentrations remained the same. Although
MTR scores increased significantly between CN1 and TV3 and between TV2 and TV4, both
the TRP and NH4 concentrations increased between those sites.
169
D2
TH5
MA4
TH6 GO6
TV6
TV5
TV4
DG2
DG1
CN1
TV1
TV2
GO1
TV3
GO2
GO3
GO4
GO5
TH1
TH2
TH3
TH4
MA2
Milltown Lake N
Figure 6.2 Map of the Milltown Lake catchment showing pairs of sites which are comparable
and showed significant up and downstream differences in MTR scores from June 2007.
Comparable pairings are given the same colour. Sites coloured grey are either not eligible for
comparison or do not have significant up and downstream differences in their MTR scores.
170
Table 6.13 Physico-chemical characteristics of up and downstream pairs of sites with a difference of greater than 4 MTR units. MTR
score are from June 2007. Physico-chemical data are medians of monthly values taken over the year May 2006 to May 2007. Substrate
indicates the most abundant type recorded. Bridge P/A indicates the presence (1) or absence (0) of a bridge.
Site MTR
TRP
mg/L
NH4
mg/L
Width
(m)
Depth
(m)
%
Riffle
%
Glide
%
Pool
Shading Substrate Bridge
P/A
Stream
Order
Slope
(m/km)
DG1 55.4 0.043 0.08 1.00 0.20 90 10 0 Med Cobble 0 1 20.00
DG2 40.0 0.080 0.07 2.00 0.15 20 70 10 High Grav/peb 0 1 16.67
CN1 54.3 0.045 0.11 0.75 0.10 20 80 0 High Cobble 1 1 14.29
TV3 63.6 0.080 0.14 2.00 0.20 20 80 0 Med Cobble 1 2 20.00
TV2 40.0 0.065 0.06 0.90 0.10 40 60 0 Med Boulder 0 1 50.00
TV4 65.5 0.077 0.17 1.50 0.15 40 60 0 Med Boulder 1 2 50.00
TV5 65.0 0.065 0.08 2.00 0.17 10 90 0 Med Boulder 1 2 10.00
TV6 58.0 0.065 0.06 1.87 0.11 70 30 0 Med Cobble 0 2 8.33
TH1 43.6 0.065 0.08 1.50 0.15 60 40 0 High Cobble 1 1 12.50
TH2 55.3 0.045 0.02 1.50 0.10 40 60 0 High Cobble 1 1 12.50
TH2 55.3 0.045 0.02 1.50 0.10 40 60 0 High Cobble 1 1 12.50
TH4 50.0 0.065 0.17 1.50 0.15 60 40 0 Low Cobble 0 1 16.67
TH5 61.8 0.055 0.07 1.25 0.10 20 80 0 Low Cobble 1 2 14.29
TH6 52.5 0.055 0.03 1.30 0.13 60 40 0 Low Cobble 1 2 7.69
171
6.5 Discussion
6.5.1 Water Quality in the Milltown Lake Catchment
Prior to the commencement of this study the available water quality data suggested the
catchment may be have been impacted. The EPA site in the catchment was classed as Q3-4
(moderate) and issues with the quality of water in the lake had been highlighted (Chapter 2).
However, in May/June 2006 and May/June 2007 all of the water quality indices assigned the
majority of sites to the higher water quality categories associated with those indices. This
suggests that the majority of sites were either unimpacted or slightly impacted. These
differences in the index scores suggested temporal variation in the water quality in the
catchment. In September/October 2006 the SSRS score placed the majority of sites in the
lowest water quality category. Although this was the only index which suggested such a high
number of impacted sites, the general trend was that a higher number of sites were allocated to
the lowest water quality category by the indices in September/October 2006. The index scores
also suggested temporal variation in water quality in the catchment. The majority of indices
identified sites in the western side of the catchment as being of poorer quality than others.
6.5.1.1 Temporal Variation in Index Scores
Seasonal variation in biological indices of water quality have been explored by a number of
authors (Zamora-Muñoz et al. 1995; Brabec & Szoszkiewicz 2006; Sporka et al. 2006;
Callanan et al. 2008). Some have attributed this variation to seasonal changes in the life cycles
of indicator communities (Brabec & Szoszkiewicz 2006; Callanan et al. 2008). Such changes
can include the emergence of particular larvae over the summer period or the increase in
species abundance of macrophytes during the growing season. However, many indices either
limit their use to a particular time of the year e.g. the MTR and CBAS (Holmes et al. 1999;
NSSHARE 2008), quantify the seasonal changes e.g. the MTR and RIVPACS (Holmes et al.
1999; Clarke et al. 2002) or incorporate such changes into the selection of indicator taxa e.g.
the Q value system, the SSRS (Clabby et al. 2008). To limit seasonal variation, RICT (River
Invertebrate Classification Tool) the tool in use by the British Environment Agency suggests
that samples taken at different times over a year be pooled (WFD UKTAG 2008b).
172
In the current study the indices which showed significant variation on either an inter-seasonal
or inter-annual basis were CBAS, the SSRS and the ASPT. Rapid responses to stressors by
macrophyte communities, such as those suggested by the CBAS scores are not necessarily
expected (Butcher 1933). It has been found that macrophyte communities are more likely to
integrate the conditions at a particular site over a longer period of time. Egertson et al. (2004)
and Sondergaard et al. (2007) suggest that the time lag for incorporation of environmental
conditions by macrophyte communities in lakes may be up to two years. Two issues
contributed to the variation in CBAS scores between sampling periods. The first issue was
related to faster growing and floating species. The algal species Lemanea fluviatilis L. was
recorded during September 2006 at TV4 but not in either of the other seasons. The floating
species Lemna minor L. was recorded at TV3 and TH3 in one or more of the seasons, but not
all. The other issue was to do with larger species which tend to establish themselves at a site.
Species such as Sparganium emersum Rehm, Callitriche obtusangula Le Gall, Alisma plantago
aquatica L. and Rorippa nasturtium aquaticum agg. were found at a number of sites in 2006
(D2, TV5, GO1, GO5, TH4 and MA4) but were absent in 2007. These species are often
associated with siltier substrates (Haslam et al. 1975; Preston & Croft 1996). The spate prone
nature of the catchment, and indeed the drumlin region (Arnscheidt et al. 2007; Douglas et al.
2007) means that sites may have been impacted by high flow conditions between September
2006 and June 2007 (Carson 2011). Although sites were not sampled during or immediately
after any large rainfall event, these conditions may have resulted in washout of the smaller
substrate particles. This may have rendered the sites less suitable for the establishment of
species associated with those smaller particle sizes and may explain their absence in 2007.
Significant seasonal variation was also evident between the SSRS scores. As the SSRS is a
rapid risk assessment tool it may be expected to respond quickly to small changes in water
quality over a short period of time (Martin McGarrigle, EPA, pers. comm.), such as those
which may be experienced during the summer months. The ASPT has been criticised for being
too sensitive to seasonal changes in the macroinvertebrate community (Zamora-Muñoz et al.
1995; Callanan et al. 2008). However this criticism was limited to the use of the index in the
summer and others have found that this variation is minimal (Armitage et al. 1983; Ruse 1996).
173
Samples in the current study were taken in early May and October and are in agreement with
the recommendations of Zamora-Muñoz et al. (1995) and Callanan et al. (2008) to reduce the
effects of summer sampling.
The decrease in index scores from May/June 2006 to September/October 2006 and subsequent
increase again in May/June 2007 suggests that there may be a trend for water quality in the
catchment to decrease over the summer and autumn and to recover during the winter and
spring. High rainfall such as that often experienced during recent Irish summers could result in
increased loading of agricultural P and N to rivers (Neill 1989; Donohue et al. 2005; Carson
2011). Studies in the Milltown Lake catchment suggest that high rainfall events were
responsible for the majority of the nutrient loading to the lake (Carson 2011). While the
seasonal trends in biological index scores parallels the trend in TRP concentrations (Appendix
2), causal relationships between changes in water quality and changes in biological indices
could only be confirmed by the analyses in Section 6.5.3 below.
6.5.1.2 Spatial Variation in Water Quality
The sites consistently ranked highest in terms of water quality by indices based on both
communities were MA4 and GO3. MA4 was located on the tributary which drained Mullyash
Mountain to the east of the catchment. The mountain top itself is under coniferous plantation
and the stream flows through agricultural land that is used for beef production before reaching
the sampling site. GO3 was located along the middle tributary. Species present at the site
included a large number of Ephemeroptera, across a number of families and a 50-75 %
covering of Chiloscyphus polyanthos (L.) Dumort. These sites had two of the lowest median
TRP concentrations in the study (GO3 = 0.038 mg/L P and MA4 = 0.041 mg/L P).
The sites consistently ranked lowest in terms of water quality in the catchment were TV1, TV2,
D1 and TH1. The sites TV1 and TH1 were located immediately downstream of their lake
sources and were selected to represent the tributary before it was subject to any of the
management practices in the Milltown Lake catchment. The paucity of macroinvertebrate
species associated with high DO concentrations (which are required to achieve a high
174
macroinvertebrate water quality index value) may be attributed to the proximity of these sites
to the lakes. Characteristics of slower flowing water which may have impacted these sites
include lower DO concentrations. The total number of macroinvertebrate taxa is known to
increase with distance from source (Robinson & Marshall 1990) and lake outlet sites such as
these can have a unique fauna (Malmqvist & Brönmark 1985) and may even constitute a
unique type (Brunke 2004). The proximity to the lake may also have an effect on the
macrophyte community as Dodkins et al. (2005a) found that DO explained significant variation
in macrophyte communities in Ireland. Another potential impact of the proximity to the lake
and slower flowing waters may be the presence of finer substrate material, which also has an
impact on macrophyte community composition (Kuhar et al. 2007; Franklin et al. 2008).
Spatial variation in water quality in the catchment was high, particularly when placed in the
context of larger monitoring programmes where single sites are selected to represent a larger
waterbody. The site GO5 is sampled by the EPA for both biological and chemical
determinands of water quality. Biological sampling is carried out once every three years and
usually in the autumn. The Q value assigned to that site for September/October 2006 was Q3-4.
The range of Q values assigned in the catchment for that period was from Q2-3 (seriously
polluted) to Q4-5 (unpolluted). This suggests that reliance on data from one sampling site in
this catchment would result in a substantial loss of information. Such spatial variation in water
quality in drumlin catchments has been observed with regard to chemical parameters
(Arnscheidt et al. 2007; Withers & Jarvie 2008). Both of those studies highlight the use of such
variation in identifying hotspots, point sources and areas for remediation. The data presented
here suggest that biological communities can be used in a similar manner to highlight areas of
better and worse water quality. However, causal relationships between changes in water quality
and changes in biological indices could only be confirmed by the analyses in Section 6.5.3
below.
6.5.2 Inter-Comparison of Water Quality Indices
None of the indices showed perfect agreement with each other, based on whether the indices
ranked the sites in the same order or whether or not the indices assigned sites to the same water
175
quality class. The two macrophyte indices were significantly correlated with each other.
However, this agreement was not perfect and suggests sources of variation between the indices.
There are a number of differences between the scores which may explain some of this
variation. Firstly, CBAS does not use species abundances, whereas the MTR does. This means
that species with low abundance are just as important in the analysis as species with high
abundance. This will have an even greater impact on the index score if the species carries a
greater weight due to trophic preferences. Secondly, the species lists used for both indices are
slightly different, with the MTR including species like Oenanthe crocata L. and CBAS leaving
it out. This may be of particular interest in the current study as this species occurs at a number
of sites, and according to the MTR, has the second highest trophic rank of any species recorded
in the catchment. While CBAS has been developed on Irish sites and any differences in species
preferences and tolerances between Britain and Ireland might be reflected in the weighting
given to species, there is disparity in the treatment of this particular species by both indices.
The distribution of O. crocata is delimited in Britain where it is concentrated the south and east
(Preston & Croft 1996), whereas in Ireland it is frequent in the south, east and north (Webb et
al. 1996). This species may exhibit different relationships with nutrient concentrations in Irish
rivers if it is more widely spread here. Further work on a wide range of sites throughout the
country and along a gradient of nutrient concentrations should establish the relationship
between species such as O. crocata and nutrient concentrations. This could then be used to
reinforce the current weighting of these species in the MTR or to propose new weightings for
CBAS.
CBAS was not correlated with any other index. However, the MTR was significantly positively
correlated with the ASPT. This is in agreement with the findings of Dawson et al. (1999) who
found a relationship of r=0.441 between the two indices at sites in England. MTR scores in that
study were all below 50 and the EQRs for the ASPT were all above 0.6. The higher EQRs, but
lower MTR scores may suggest that this study was carried out on lowland sites as lowland sites
can have a naturally lower MTR without suggesting impact from eutrophication. The range of
MTR scores in this study was higher than that of Dawson et al. (1999), suggesting that the
studies were carried out on rivers of a different type. However, the relationship between the
176
MTR and the ASPT was remarkably similar (r=0.496). This may suggest that the MTR and the
ASPT can provide reasonable agreement across a range of river types.
All of the macroinvertebrate indices were strongly positively correlated (r>0.7) suggesting
good agreement between them based on site rankings. While information from the same dataset
might be expected to produce the same answer, each of the indices used were either developed
for different purposes or were developed in a different ecoregion. The Q-value and the SSRS
were both developed in Ireland, but for different purposes. The Q-value requires greater
taxonomic knowledge, while the SSRS was developed as a rapid bankside risk assessment tool.
The treatment of stoneflies and mayflies by both indices highlights how potential differences
might occur between scores. The SSRS assigns all stonefly families the highest ranking and all
mayfly families the second highest (WRBD 2005). However, the Q-value ranks the Leuctridae
and Nemouridae second and the Caenidae third (Toner et al. 2005). The BMWP and the ASPT
were developed in Britain. Due to differences in our glacial history different macroinvertebrate
assemblages might be expected between the two countries (Giller et al. 1998). While the ASPT
score is derived from the BMWP, differences between those scores can arise where species
rich sites drive up the BMWP score. This is compensated for by the ASPT by dividing the
BMWP by the number of scoring families. Both of the indices developed in Ireland showed
better agreement with each other than with those developed in Britain and vice versa. This may
be related to either the different weighting given to species in the indices or differences in the
expected taxa between the two countries. However, the good level of agreement between the
indices from the two countries suggests that these differences are not great.
The level of agreement between indices based on the traffic light system changed with the
season as some indices showed significant variation with season and others did not. However,
the indices did show some agreement in placing sites in the green or red category. This has
been shown to be the case with a number of indicators where intercalibration at EU level was
only undertaken at the good/moderate boundary as there was poorer agreement at the others
(McGarrigle & Lucey 2009). Perfect agreement between indices is not evident in the literature
as often the indices used target different communities (Beyene et al. 2009; Torrisi et al. 2010)
or measure different pressures (Fabris et al. 2009). Limitations to the methods used in the
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current study must be acknowledged. The indices selected were not designed to be directly
comparable. Furthermore, there is difficulty associated with using the MTR scores to provide
information as to the extent of impact to sites with scores between 25 and 65. While placing
scores in context with others from the same catchment is recommended to deal with this
(Holmes et al. 1999), this essentially results in a ranking of sites based on the highest score
obtained in the catchment rather than absolute values. In a number of instances a standard
descriptor of 6 had to be assigned to the sites, this means that the MTR could not be used to
provide information on the extent of impact to the site. This occurred where there were no
other sites of similar river type in the catchment against which to compare the score. The MTR
was designed to be used in up and downstream comparisons of sites of a similar physical type
so issues such as the one encountered in this ‗eutrophication audit‘ would not have been of
primary concern. Studies which seek to use the MTR in catchment studies should ensure that
all river types are represented by a number of sites so that comparisons can be made.
Another issue which hampered the comparison of scores was the lack of any really low water
quality sites in the catchment. This meant there was not a wide gradient along which to test the
response of indices and from which a third ‗poor‘ or ‗bad‘ category could have been
developed. However, even where such issues between indices do not exist agreement is not
necessarily perfect (Birk et al. 2006; Schneider 2007). The current intercalibration process for
WFD ecological assessment tools requires a relationship of only r2=0.5 between member state
metrics and the selected common metric, as it has not been possible to find stronger
relationships across the board (Birk & Willby 2010). In the current study better within
community agreement was hoped for, based on water quality categories assigned. However,
the ability of each of the biological elements to highlight different pressures to the system and
perhaps on different time scales may be seen as a strength of the approach. While the
implications of poor agreement between indices may include decreased confidence in the
classification of a water body (Moss et al. 2003; Hatton-Ellis 2008), the use of a number of
communities, able to respond to a number of pressures may decrease the chance of
misclassifying a water body where there is a pressure.
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6.5.3 Comparison between Water Quality Indices and Physico-chemical
Parameters
As the indices were applied to provide information on water quality in the catchment it was
expected that they would show relationships with known physico-chemical parameters of water
quality. Furthermore, the use of the physico-chemical parameters as common metrics against
which the indices could be compared allowed further assessment of their performance. The
MTR was designed to detect impact by eutrophication downstream of a point source (Holmes
et al. 1999) and has been shown to correlate with P concentrations (Dawson et al. 1999). CBAS
was designed to assess ecological quality in fulfilment of the requirements of the WFD and
relationships between scoring species and P, nitrate, ammonium, siltation, DO and pH are used
to calculate the species scores (NSSHARE 2008). The Q-value measures the response of
macroinvertebrate communities to organic pollution and eutrophication and includes DO
concentration as part of the decision process in assigning water quality classes. The Q-value
has also shown a significant relationship with TRP in two studies which used data from 1000
and 797 sites throughout Ireland respectively (McGarrigle 1998; Donohue et al. 2006). The
SSRS was developed as a risk assessment tool and its format was optimised to provide
significant correlations with not only metrics being used in the EU e.g. ASPT, but also with
TRP and nitrogen (WRBD 2005). The BMWP and the ASPT were derived from expert opinion
(Hawkes 1998), but have been correlated with measured physico-chemical parameters
(Armitage et al. 1983).
In the current study the MTR was significantly correlated with TRP. The MTR has been shown
to correlate with P, especially at concentrations of <1 mg/L P (Dawson et al. 1999) and with
total ammonia, total P, total N and DO (Szoszkiewicz et al. 2002). Demars & Edwards (2009),
working in Britain and Aguiar et al. (2011), working in Portugal have reported unexpected
relationships between the MTR and the measured physico-chemical parameters. In the case of
Demars & Edwards (2009) the effects of nutrients P and N could not be separated from effects
of site characteristics and in the case of Aguiar et al. (2011) only weak relationships between
the measured parameters were found. While the relationship between the MTR and TRP was
stronger than the relationship found by Aguiar et al. (2011) (r=0.35-0.4), the correlation
coefficient suggests that there are other sources of variation in the MTR score. As the physical
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characteristics of the sites were shown to explain variation in the macrophyte community in
Chapter 3, the results of this study may show some agreement with those of Demars &
Edwards (2009). The relationship between the MTR and TRP suggests the potential of the
MTR to reflect differences in water quality in the catchment.
Significant up and downstream changes in MTR scores were found to coincide with increases
and decreases in TRP concentrations at most of the sites. Three exceptions to the expected
reciprocal relationship between the MTR and TRP were found on the TV tributary.
Downstream increases in MTR scores were coincident with downstream increases in TRP
between CN1 and TV3 and between TV2 and TV4. Downstream decreases in MTR scores
were coincident with downstream decreases in TRP between TV5 and TV6. This disagreement
between the index scores and the chemistry suggests that there may be other drivers of the
MTR score on that tributary.
TRP was significantly negatively correlated with all of the macroinvertebrate indices.
Conductivity was significantly correlated with all of the macroinvertebrate indices but the
ASPT. However, this relationship was very close to significant at the 5 % level (p=0.051).
Ammonium was significantly negatively correlated with the Q-value. These relationships
suggest that TRP inputs to the catchment are impacting the macroinvertebrate fauna. The
relationship between the Q-value system and conductivity has been shown to be significant
across Ireland (Donohue et al. 2006). The response of the Q-value to conductivity
concentrations was attributed in that study to the relationship between conductivity and the
intensity of land use. The relationship between macroinvertebrate indices and ammonium has
also been highlighted by a number of studies and underlies the ability of the macroinvertebrate
community to respond to organic pollution (McGarrigle 2001; Murphy & Davy-Bowker 2005).
The significant relationship between the macroinvertebrate indices and parameters of water
quality in the catchment, particularly P, suggests their potential to represent water quality in the
catchment.
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6.6 Conclusions
6.6.1 To assess water quality in small first and second order streams in a drumlin
catchment using macroinvertebrate and macrophyte indices commonly in
use in Britain and Ireland
Scores derived from the water quality indices suggest both temporal and spatial variation in
water quality in the catchment. All indices suggested poorer water quality conditions in
September/October 2006. The results imply that carrying out assessments when the impact of
pressures is greatest may give a more useful picture of water quality to catchment managers as
this represents a worst case scenario. The spatial variation in water quality suggested by the
biological indices may have implications for larger monitoring programmes. Reliance on data
from one sampling site in this catchment, as the EPA does, would result in a substantial loss of
information. Such spatial variation in water quality in small drumlin catchments has been
observed with regard to chemical parameters (Arnscheidt et al. 2007). That study highlighted
the use of such variation in highlighting hotspots, point sources and areas for remediation. The
data presented here suggest that biological communities can be used in a similar manner to
highlight areas of better and worse water quality. However, the relationship between the index
scores and water quality was not confirmed for all of the indices.
6.6.2 To compare the results of water quality assessments of the Milltown Lake
Catchment as determined by the macrophyte and macroinvertebrate indices
Agreement between site rankings by the indices varied. Agreement between the
macroinvertebrate indices was higher than between the macrophyte indices. The only indices
that showed agreement between communities were the MTR and the ASPT. The level of
agreement between the indices based on the traffic light system was low, both within and
between communities. While the implications of poor agreement between indices may include
decreased confidence in the classification of a water body (Moss et al. 2003; Hatton-Ellis
2008), the use of a number of communities, able to respond to a number of pressures decreases
the chance of misclassifying a water body where there is a pressure.
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6.6.3 To examine correlations between the various indices and the measured
physico-chemical parameters of the water
The MTR and all of the macroinvertebrate indices were all significantly correlated with
TRP. Some macroinvertebrate indices were also correlated with ammonium (Q-value)
and conductivity (Q-value, SSRS and BMWP). CBAS was not significantly correlated
with any of the physico-chemical parameters of the water column. The significant
relationship between the MTR and the macroinvertebrate indices and parameters of
water quality in the catchment, particularly P, highlights their potential as water quality
indicators. Furthermore, the pattern of variability in water quality identified by the
indices, both spatially and temporally suggests that these indices could be used to
highlight pollution hotspots within small drumlin catchments such as this. The lack of a
relationship between CBAS and the physico-chemical parameters suggests that CBAS
did not reflect the water quality in the Milltown Lake catchment. As such it could not
be recommended for use in water quality assessments in small catchments along the
drumlin belt.
It was easier to compare index results between the sites using the physico-chemical
parameters as common metrics than when the traffic light system was used. Limitations
to the methods used in the current study must be acknowledged. The indices selected
were not designed to be directly comparable. Another issue which hampered the
comparison of scores was the lack of any really low water quality sites in the catchment
meant there was not a wide gradient along which to test the response of indices and
from which a third ‗poor‘ or ‗bad‘ category could have been developed.
Significant up and downstream changes in water quality followed expected patterns of
nutrient concentrations in the TH and DG tributaries. However, exceptions to the
expected inverse relationship between the MTR and TRP were found on the TV
tributary. This suggests that there may be other drivers of the MTR score on the TV
tributary and that the MTR may not be suitable to assess the impacts of those drivers.
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Chapter 7 Drivers of MTR Score in the Milltown Lake Catchment
_____________________________________________________________________________
7.1 Introduction
Various methods of analysis have been used in Chapters 3 to 6 to provide data with which to
address the central aim of this thesis: to assess the use of macrophytes as biological indicators
of water quality within a small catchment. Although these analyses showed significant
relationships between macrophyte community composition and the physico-chemical
parameters of the water quality, a large proportion of the variation in the macrophyte
community data was not explained (>65 %). The classification results from Chapter 3 showed
that the physical characteristics of the sites were significantly different between the cluster
groups. This may suggest that these characteristics could explain further variation in the
macrophyte community. Indeed, the impact of site physical characteristics on macrophyte
communities is well acknowledged (Butcher 1927; Holmes et al. 1999; Demars & Edwards
2009). The analyses presented in Chapter 6 showed a significant relationship between the MTR
and TRP. The relationship was significant, and the r value obtained (r=-0.515) indicated that
27% of the variation was explained by this one parameter. However, this also indicated that
there are further sources of variation in the MTR scores. While other studies have found weaker
correlations between the MTR and phosphorus (e.g. Aguiar et al. 2011), Demars & Edwards
(2009) found a correlation of r=0.69, suggesting that stronger relationships might be possible.
Thus, the aim of this chapter is to assess spatial distribution of species, scores and possible
drivers in more detail and to ask, if relationships between the macrophyte water quality indices
and the physico-chemical parameters are weak, what is driving the differences in index scores?
7.2 Variation in MTR Scores
Comparison of the MTR scores with the annual median TRP concentrations in Chapter 6
showed an overall trend of decreasing TRP with increasing MTR score. However, there are a
number of sites at which the data did not show agreement with this trend. The sites TV3, TV4
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and TV5 all have MTR scores in excess of 63, but the TRP concentrations for these sites are
nearly double the Ecological Quality Standard (EQS) for the good/moderate boundary (0.035
mg/L) (Appendix 2). Ammonium concentrations at these sites are also quite high and at TV3
and TV4 they are more than double the EQS for the good/moderate boundary (0.065 mg/L N)
(Appendix 2). Table 7.1 shows summary statistics for each of the main tributaries in the
catchment, highlighting spatial variation in the water quality parameters. These data show that
the TV tributary had the highest TRP, conductivity and ammonium concentrations. High
ammonium concentrations can suggest organic pollution at a site (EPA 2001). The MTR
manual suggests that where organic pollution is suspected, assessments of diatoms or
macroinvertebrates be used. This is because the response of the MTR has not been investigated
with regard to organic pollution. Macroinvertebrate assessments of the tributary suggested
impact from organic pollution at site TV1 (Q-value of 2-3). Assessment of diatom assemblages
at sites TV2 and TV3 showed no decrease in the Trophic Diatom Index score (Sweeney 2011).
However, there was an increase in the number of motile valves, which indicates human
disturbance, and sewage fungus was noted by that author at TV2 during the winter of 2006.
Table 7.1 Summary statistics for physico-chemical parameters for sites from each of the three
main tributaries in the Milltown Lake catchment.
Cond.
µS/cm
pH Alk.
mg/L
CaCO3
DO % BOD
mg/L O2
NH4
mg/L
TRP
mg/L
TV Mean 174.3 7.57 64.00 86.29 1.97 0.093 0.065
Median 164.2 7.57 66.00 87.85 1.90 0.080 0.070
Min. 143.5 7.19 44.00 65.80 1.59 0.060 0.040
Max. 219.5 7.85 84.00 95.20 2.74 0.170 0.080
N 11 11 11 11 11 11 11
GO Mean 148.8 7.51 58.33 88.93 2.24 0.043 0.047
Median 143.2 7.46 58.00 87.55 2.21 0.045 0.045
Min. 126.7 7.43 48.00 85.00 1.29 0.020 0.030
Max. 190.8 7.69 70.00 93.60 3.28 0.070 0.070
N 6 6 6 6 6 6 6
TH Mean 140.3 7.52 49.50 80.94 2.15 0.089 0.061
Median 137.0 7.50 51.00 82.45 2.28 0.075 0.055
Min. 106.4 7.39 42.00 66.70 1.19 0.020 0.040
Max. 173.2 7.67 54.00 86.00 2.94 0.180 0.130
N 8 8 8 8 8 8 8
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The spate prone nature of the Milltown Lake catchment and the steep slope along much of the
TV tributary may have a role to play in explaining the high MTR scores and high TRP and NH4
concentrations. Disturbance due to intermittent high flows and increased current velocity at
high gradient sites can affect the biota of rivers and streams (Barrat-Segretain 2001; Riis &
Biggs 2003; Cardinale et al. 2005). Increased current velocity can affect the ability of algal
species to establish, and in such conditions the community can show closer relationships with
current velocity than nutrient concentrations (Biggs 1995; Gustina & Hoffmann 2000).
McGarrigle (2001) suggests that in systems prone to flash flooding although there is available
P, the response of aquatic communities will be dominated by the current velocity regime rather
than the instream nutrient conditions. This means that although P concentrations will be high,
the usual evidence of eutrophication, including high plant and algal biomass and diel oxygen
fluctuations, will be absent.
Hygrohypnum luridum, Chiloscyphus polyanthos, Brachythecium rivulare and Oenanthe
crocata were all recorded on the TV tributary. These species have high trophic rankings in the
MTR (9, 8, 8 and 7 out of 10 respectively). High scores at sites TV3 to TV6 are due to the
presence of these species. However, not all of these species showed significant relationships
with the measured physico-chemical parameters (Appendix 2). H. luridum and C. polyanthos
were not correlated with any of the measured physico-chemical parameters and so their
indicator potential could not be confirmed from this study. B. rivulare was significantly
negatively correlated with TRP concentrations (r=-0.648, p=0.001). This suggests that the
results of the current study are in agreement with the ranking given to this species by the MTR.
O. crocata showed the highest number of significant correlations with the physical
characteristics. Its distribution was significantly positively correlated with the slope categories
for the Irish river typologies (Kelly-Quinn et al. 2005), river width, stream order and the
presence of a bridge (r=0.413, p=0.045, r=0.541, p=0.006, r=0.539, p=0.007 and r=0.442,
p=0.031 respectively). These significant relationships between O. crocata and the physical
characteristics suggest that they are playing a greater part in its distribution than the nutrient
concentrations. Other members of the Umbelliferae that share many morphological
characteristics with O. crocata have been shown to be well suited to high current velocities.
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Barrat-Segretain (2001) found that Berula erecta (Hudson) Cov. showed significant plasticity in
biomass allocation above and below ground depending on levels of flood disturbance in the
Rhine valley. This allocation of biomass below the ground allows certain species to thrive in
intermittently flooded conditions. Butcher (1933) also suggests that B. erecta might be well
adapted to high current velocities and high flows due to its small leaves and strong stems.
Furthermore, Holmes (2009) suggests that O. crocata is an upland species. Upland species are
often adapted to steeper gradients and higher velocities (Baattrup-Pedersen et al. 2006; Scarlett
& O‘Hare 2006).
The relationship between the MTR at these sites, the nutrient concentrations and river type
might suggest one of two scenarios. The first may be that the indicator potential of O. crocata is
poorly understood in Ireland, and perhaps Britain. The published habitat preferences of O.
crocata can be contradictory. Haslam (1987), Dawson et al. (1999) and Lansdown (2008) all
suggest that it is a marginal plant, rarely found in water. However, Holmes (1999; pers. comm.
in Lansdown 2008) suggests that it can also grow as an aquatic plant. Also, the reported nutrient
preferences of this species seem contradictory. Haslam (1987) suggests it is associated with
oligotrophic sites but Haury (1995), working in France, refers to it as a ‗tolerant downstream
species‘. The relationship between upland species and low nutrient concentrations may be
confounded by the general trend for upland sites to be lower in nutrient concentrations due to
lack of settlement, poor suitability for agriculture, industry and general accessibility by man and
his pollutants (Allott et al. 1998). Plants that find their niches in such river types may be
responding more to the gradient and current velocity than to the nutrient concentrations. It was
suggested in Chapter 6 that nutrient optima for each species be assessed in Ireland to assist in
refining scores and weightings and to make the MTR and CBAS more applicable here.
However, the impact of O. crocata on the relationship between the MTR score and TRP was
not assessed. The strength of the correlation between the MTR and TRP increased from an r
value of -0.515 to -0.733 (p=<0.05) when the sites at which O. crocata was recorded were
removed from the analysis, indicating that 50 % of the variation in MTR scores could now be
explained by TRP concentrations. However, when alternative trophic rankings were applied for
O. crocata, MTR scores did not decrease greatly (Table 7.2). When O. crocata was not
included in the MTR calculation, scores did not decrease greatly either (Table 7.2). From this it
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was concluded that although the presence of O. crocata indicated the importance of the
physical characteristics of the sites, it alone was not responsible for the mismatch between the
MTR scores and the nutrient concentrations.
Table 7.2 Alternative MTR scores for sites based on changes to the trophic ranking of Oenanthe
crocata L. Numbers refer to the trophic ranking given to the species in the MTR calculations.
No O. crocata refers to scores derived when the species was omitted from the analysis.
Site O. crocata 6 O. crocata 5 O. crocata 7* O. crocata 8 No O. crocata
TV3 60.0 56.0 63.6 67.3 60.0
TV4 64.4 63.0 65.5 66.7 65.0
TV5 61.0 58.3 65.0 68.3 62.5
* This is the trophic ranking given to O. crocata by the MTR (Holmes et al. 1999)
The second factor to which the relationship between the MTR at these sites, the nutrient
concentrations and river type might be attributed is related to the availability of data on
reference conditions. Sites with steep slopes (greater than 40 m/km), such as TV2 and TV4,
may be poorly described by the Irish river typology (Kelly-Quinn et al. 2005). In fact, there was
only one reference site for this river type used to develop the Irish river typology. That site was
assigned an MTR score of 65 in that study (see Table 6.4). Some upland sites (which are often
associated with steep slopes and high current velocities) can achieve much higher MTR scores
than those presented by either the current study or Kelly-Quinn et al. (2005). The mean of the
top 10 % of MTR scores for some of the upland river types in Britain are in the range 75 – 86.2
(Holmes et al. 1999). If the range of MTR scores for reference condition steep sloped sites in
Ireland is wider than the data of Kelly-Quinn et al. (2005) suggest, the implications might be
that the sites in the current study have been misclassified by comparing their scores with just
one reference condition site. The sites GO3 and GO4 were also a steep gradient sites (slope
greater than 40 m/km). These sites had MTR scores of 70.8 and 68 respectively in June 2007.
Although TRP concentrations at these sites were much lower than on the TV tributary (0.038
and 0.034 mg/L P respectively), they were still in excess of the EQS for the good/moderate
boundary. This provides further evidence that sites of this river type, with MTR scores in
excess of 65, may be impacted by eutrophication.
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Other exceptions to the relationship between high MTR scores and low TRP concentrations can
be seen at sites TH2, GO2, DG1 and CN1. Each of these sites has lower TRP concentrations
than the ranking of their MTR score might suggest. At both TH2 and GO2 this lower score
might be attributed to the effects of having a poorer quality site located upstream. While
nutrient concentrations can be quite variable along a river (Wilcock et al. 2004; Withers &
Jarvie 2008), improvements in the macrophyte community downstream of an impact are also
complex (Holmes et al. 1999). Demars & Harper (2005) suggest that these improvements will
be affected by the dispersal and regeneration abilities of the macrophyte community. As a result
the time (and distance) needed for recovery of the water chemistry and the flora may be quite
different (Egertson et al. 2004; Sondergaard et al. 2007). There were no upstream sites on the
DG1 and CN1 tributaries against which to compare them. However, there were incidents of
extremely high TRP concentrations during the year May 2006 to May 2007 at both of these
sites, including a concentration of 0.5 mg/L P at CN1 in August 2006 (Appendix 2).
7.3 Conclusions
The MTR has been extensively trialled for use throughout Europe to fulfil the requirements of
the WFD and is currently under trial by the EPA in Ireland. However, it has not been adopted
in Britain where LEAFPACS, a tool with a predictive element to allow comparison across
types is now in use (WFD UKTAG 2008a). The relationship between the MTR and TRP
concentrations in this catchment suggests that this method has potential for bioindication of
trophic status in similar catchments. However, the use of this method to provide information
on the water quality at a particular site in absolute terms is complicated by a number of
factors. The availability of data on reference condition sites is critical for the interpretation of
MTR scores. Some of the river types in the current study were poorly represented in the
development of the Irish river typology as candidate reference sites were not found.
Furthermore, reference conditions for sites in the drumlin landscape may be particularly
difficult to describe due to the impacts on water quality of the intensive agricultural practices
in the area as a whole. If reference conditions cannot be adequately described, the usefulness
of the MTR for such assessments must be questioned. This may have implications, not only
for steep gradient sites in the drumlin belt, but also for any of the river types that were poorly
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represented in the assessment of the Irish typologies i.e. low alkalinity, high gradient sites and
high alkalinity sites of all slope categories (Kelly-Quinn et al. 2005).
Until reference conditions can be adequately described for all river types, the use of the MTR
must be limited to those river types for which adequate data exists. As the drumlin landscape is
dotted with sites of varying degrees of slope, site selection should be limited to those river types
for which reference conditions are adequately described. This suggests that sites with slopes of
greater than 40 m/km should not be included for monitoring. This restriction may limit the
usefulness of the MTR in some catchments in the region and in particular, on a number of
tributaries. Maps provided by Kelly-Quinn et al. (2009) suggest that the river types most
sparsely represented in the dataset of reference sites are those that are sparsely represented in
the national monitoring program. This might suggest that the MTR could still be used at a large
proportion of the sites within the national monitoring program.
Where the MTR is used, a number of (physically and typologically similar) sites in the same
catchment should also be assessed as stated by Holmes et al. (1999). This helps to place the
MTR scores into context and to judge the degree of impact at the site. While this will be helpful
when interpreting scores, it greatly increases the costs associated with using this assessment
method. Where resources are already strained, adding extra sites to the monitoring program
may not be possible. While the use of macrophytes to assess water quality is required of the
EPA by the WFD, smaller monitoring programs will not be under such constraints. If resources
cannot be found to conduct MTR surveys at extra sites, the use of indices based on the
macroinvertebrates (e.g. the Q-value, the SSRS) may be preferable as these can provide results
which can be interpreted independently.
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Chapter 8 General Discussion
_________________________________________________________________
8.1 Context and Aims
Surface waters in Ireland, and indeed across much of the developed world, are at risk from the
effects of anthropogenic activities (Schindler 2006; Kelly 2009). Industry, agriculture and
domestic waste and wastewater contribute to the eutrophication, acidification and physical
degradation of rivers and lakes (Lucey 2009). Although physico-chemical monitoring forms
an integral part of the monitoring programs of many countries, it is understood that such
methods can only provide a snapshot of the conditions in a given waterbody at the time of
sampling. The Water Framework Directive (WFD) (CEC 2000) has generated much interest
across the world as an ambitious plan to integrate a number of biological elements and their
responses to a number of pressures (e.g. eutrophication) into monitoring programs. Although
there is a requirement for elements such as the macrophytes, phytobenthos and fish to be used
as biological indicators in each of the member states of the European Union, tools based on
these communities either have not yet been developed for each country or are under trial. This
suggests that the potential for use of these elements may be poorly explored in member states
and that the tools adopted will require testing within the target countries (McGarrigle & Lucey
2009).
The fundamental aim of this study was to evaluate macrophytes as biological indicators in a
small drumlin catchment and determine whether they could reliably detect degradation and
discriminate between sites, both spatially and temporally. Extensive and repeated site
monitoring is especially important in a catchment such as Milltown where the detection of the
location and period of impact is crucial for determining what measures might be applied to
protect the water supply. The objectives of this study were addressed by assessing the
relationship between the macrophyte community and environmental parameters and by
comparison with an established bioindicator group in Ireland, the macroinvertebrates.
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8.2 Synopsis of the Results
The macrophyte community recorded in the current study is characteristic of the basic geology
and circum neutral waters of the Milltown Lake catchment. Species composition at the sites
suggests that the macrophyte communities of these drumlin streams may be intermediate
between the upland headwater communities and lowland communities of large slow flowing
rivers described in the literature. Such intermediate stream types may not be adequately
described by the WFD typology (Baattrup-Pedersen et al. 2006). Some of the river types in the
current study were poorly represented in the development of the Irish river typology as
candidate reference sites have not yet been identified (Kelly-Quinn et al. 2005; Kelly-Quinn et
al. 2009). Furthermore, reference conditions for sites in the drumlin landscape may be
particularly difficult to describe due to the impacts on water quality of the intensive agricultural
practices in the area as a whole (Gibson et al. 1980; Gibson et al. 2003).
The current research suggests that the bank macrophyte community was structured by the
physical characteristics of the site rather than the physico-chemical parameters of the water.
Significant differences between cluster groups were not related to the physico-chemical
parameters of the water quality and ordination of the bank macrophyte dataset and the physico-
chemical data did not produce a significant model. Furthermore, the bank macrophyte dataset
and the aquatic dataset showed low levels of community concordance. Low community
concordance suggests that the bank macrophytes are not responding to the same environmental
gradients as the aquatic macrophytes. This suggests that the bank macrophytes should not be
used in assessments of water quality. However, the significant relationship between the bank
macrophyte dataset and the physical characteristics of the sites, including hydromorphological
characteristics may indicate the potential for bank species to be used in the assessment of
hydromorphological pressures.
The ordination analysis showed that the parameters which explained the most variation in the
aquatic macrophyte community composition were TRP, NH4 and TDS. The ranges of TRP and
NH4 recorded for the Milltown Lake catchment were wide enough to structure the macrophyte
vegetation as they were straddled the good/moderate boundaries of the ecological quality
standards (EQS). The ability of TDS (or its correlate conductivity) to explain variation in
191
macrophyte communities has been attributed to its relationship with enrichment by the major
ions P and N (Ecke 2009) and in fact, could not be separated from such pressures by Demars &
Edwards (2009) when trying to rank and separate the individual effects of local environmental
conditions on macrophyte species distribution. While these parameters explained significant
variation in the aquatic macrophyte community, the majority of the variation was not explained
(~65 %).
Variation in the aquatic macrophyte dataset was also significantly related to the physical
characteristics of the sites. These characteristics could be related to increasing stream size and
habitat heterogeneity, which have been shown to affect species diversity and abundance in
other studies (Beisel et al. 2000; Baattrup-Pedersen et al. 2006). The importance of site
physical characteristics in shaping macrophyte communities is evident in the literature
(Chambers et al. 1991; Kuhar et al. 2007; Grinberga 2010). Daniel at al. (2006) also carried
out a survey of macrophytes and related them to the physical characteristics of the sites. They
found that plant morphology could be predicted from the site physical characteristics. This is
in agreement with the ‗habitat templet‘ theory (Southwood 1988), which suggests that species
life history strategies are related to habitat. Daniel et al. (2006) further suggested that
knowledge of the relationships between macrophytes and site physical characteristics could
provide information on site specific reference conditions.
The significant relationship between the MTR and the macroinvertebrate indices and
parameters of water quality in the catchment, particularly P, highlights their potential as
indicators of trophic status. However, exceptions to the relationship between high MTR scores
and low TRP concentrations were seen at a number of sites. These exceptions suggested that
type specific reference conditions may not have been well described for all of the river types
surveyed in the Milltown Lake catchment. If reference conditions cannot be adequately
described, the usefulness of the MTR within those river types must be called into question.
The pattern of variability in water quality identified by the indices, both spatially and
temporally suggests that they could be used to highlight pollution hotspots within small
drumlin catchments such as this. However, the CBAS index was not significantly correlated
with any of the physico-chemical parameters of the water column. This suggests that CBAS
192
did not reflect the water quality in the Milltown Lake catchment. As such it could not be
recommended for use in water quality assessments in small catchments along the drumlin belt.
Physical properties such as substrate size showed significant differences between
classification groups. This may suggest that sediment characteristics play an important part in
structuring the macrophyte community. The current research is in agreement with the findings
of Clarke & Wharton (2001) and Schneider & Melzer (2004) in finding little difference
between sediment samples collected either early or late in the growing season and little
difference between sediment samples collected from vegetated and unvegetated areas.
Classification analyses showed little spatial variation in the sediment characteristics between
the sites in the Milltown Lake catchment. This may indicate that catchment scale processes are
important in governing river sediment characteristics. Furthermore, low variation in sediment
characteristics may suggest that sediment characteristics will not play an important role in
structuring macrophyte community composition across the catchment.
8.3 Implications for Management
Due to economic constraints on national monitoring programs smaller first and second order
streams are often neglected in favour of larger downstream sites. These downstream sites are
selected to represent the whole river system, but small first and second order streams
constitute as much as half of the country‘s river network. As WRBD (2005) caution, if the risk
of water pollution is proportional to the length of the river network then these small streams
may provide a conduit whereby half the discharges to rivers occur. For programs of
remediation measures to effectively target the sources of nutrient inputs, assessment of smaller
first and second order streams may be necessary to pinpoint problem areas. This may be of
particular importance in the drumlin belt with its intricate drainage pattern of small first and
second order streams. For assessment of these streams to provide meaningful results, the tools
used must have proven ability in describing the type and magnitude of the pressures in such
streams. Chapter 1 outlined the attributes of a good biological indicator as one that is easy to
collect and identify, responsive to a number of different pollutants or stressors along a gradient
of that pollutant or stressor, with these responses being well documented in the literature.
193
Furthermore, good biological indicators can be used to provide information on water quality
that has been assimilated over a longer period of time than that which can be gathered by on-
off chemical sampling means (De Pauw & Persoone 1979; Metcalfe 1989; Rosenberg & Resh
1993).
There was little variation in the macrophyte community in the Milltown Lake catchment over
the growing season. This may be due to the characteristics of species recorded in the
catchment, but also to the type of substrate and current velocity. As abundant growth of
macrophytes is associated with siltier substrates (Dodkins et al. 2005a; Grinberga 2010), the
dominance of cobble and gravel/pebble at many of the sites surveyed in the Milltown Lake
catchment may have limited the growth of species that often reach high abundances in rivers
e.g. Sparganium emersum Rehm., Callitriche obtusangula Le Gall, Elodea canadensis Michx.
or discouraged the establishment of other species associated with siltier substrates and high
seasonal abundances e.g. Ranunculus spp. (Butcher 1933; Haslam et al. 1975). This low
temporal variability suggests that macrophyte surveys of the Milltown Lake catchment could
be compared with those of other surveys without impact from temporal variation. This would
place fewer restrictions on survey planning and may be of particular use in such spate prone
catchments as poor survey conditions could be avoided when necessary without the fear of
introducing temporal variation. However, in some river types growth of macrophytes occurs to
such a degree that navigation and flow are impeded (Cotton et al. 2006; Franklin et al. 2008)
and temporal variation was found to be one of the most important sources of variation in river
macrophyte surveys across Europe (Brabec & Szoszkiewicz 2006). As such this finding may
not have implications for all river types. Temporal variability in some of the water quality
indices (ASPT and SSRS) suggested poorer water quality in the catchment during the
September/October sampling period. Carrying out assessments when the impact of pressures
is greatest may give a more useful picture of water quality to catchment managers as this
represents a worst case scenario.
The poor ability of the bank macrophyte taxa to reflect water quality suggests that they are not
good indicators of water quality. This further indicates that the extra effort involved in the
recording, identifying and analysing of such data would not be warranted for water quality
194
assessments in the Milltown Lake catchment. The reduction in species list achieved by not
surveying the bank species creates not only efficiencies in the field but is in agreement with the
approach taken by other monitoring methods (e.g. Holmes 1999).
The positive relationship between the aquatic macrophytes and both the physical
characteristics of the sites and the physico-chemical parameters of the water suggests that
although the community retains potential to respond to nutrient concentrations, sites must be
as physically and typologically similar as possible to allow comparison of macrophyte
communities. Where greater physical similarity between sites exists, the portion of variation
attributed to the physico-chemical parameters may increase and the indicator potential of the
community with it. The variable topography in the drumlin landscape creates rapid changes in
flow and hydrology. This has implications for site selection when monitoring drumlin
catchments. If physically similar sites cannot be found, comparisons should not be made
between sites. The issue of physical comparability may have wider importance where
macrophyte surveys are being added to existing monitoring programs, such as that carried out
by the EPA. Sites which are currently being sampled for macroinvertebrates may not have
enough physical characteristics in common to allow macrophyte assessments to be compared.
This may introduce error into the comparison of ecological status between sites. Furthermore,
where type specific tools are being used, the poor description of reference conditions for some
of the river types in the drumlin region may limit the sites and streams which can be assessed.
This is because the consequences of poorly described reference conditions can include the
misclassification of sites using type specific monitoring tools. Maps provided by Kelly-Quinn
et al. (2009) suggest that the river types most sparsely represented in the dataset of reference
sites for the country are those that are sparsely represented in the national monitoring program.
This might suggest that type specific monitoring tools could still be used at a large proportion
of the sites within the national monitoring program.
8.4 Limitations of the Current Research
Macrophyte and macroinvertebrate community data were compared in the current study using
a number of approaches. However, the method of collecting these data may have introduced
195
its own source of variation into the analyses. Macrophyte surveys were conducted over 100m
stretches and all macrophytes growing in all habitats were recorded. Macroinvertebrates were
collected only from riffles within these sites and the proportion of riffle at each site varied.
Macrophyte habitat preferences can be so marked that Daniel et al. (2006) found plant
morphology could be predicted from the site physical characteristics. Higher concordance
might have been found between the two communities if all habitats in the macrophyte
sampling area had been sampled for macroinvertebrates.
Although the relationship between the macrophytes and sediment characteristics was assessed
by the analysis of vegetated and unvegetated sediment, it was not possible to assess species
preferences for particular sediment conditions. This was due to the approach taken of pooling
sediment replicates. As replicates were to be pooled, species with which the sediment was
associated were not recorded. Furthermore, records of the percentage of each substrate type at
a site and substrate type from which each species was recorded may have allowed variation in
the macrophyte community to be assessed with regard to sediment characteristics.
In addition to these surveying issues, the biological water quality indices used were not
designed to be directly comparable. Difficulties in comparing the output of various indices of
water quality have also been encountered during the process of intercalibration for the WFD.
This has shown that the results of any comparison of sites, like for like, can be dependent on
the method chosen (McGarrigle & Lucey 2009). The use of the red and green water quality
categories did not allow an assessment of the ability of the indices to detect the magnitude of
an impact at the sites. However, this approach was supported by the use of correlation
analyses between the indices themselves and between the indices and the physico-chemical
parameters of the water column. Another issue which hampered the comparison of scores was
the lack of any really low water quality sites in the catchment. This meant there was not a
wide gradient along which to test the response of indices and from which a third ‗poor‘ or
‗bad‘ category could have been developed. Despite these caveats the study has shown that the
MTR and all of the macroinvertebrate indices were significantly correlated with physico-
chemical parameters of water quality (TRP).
196
8.5 Knowledge Gaps and Future Research
The work described in the current study has highlighted several gaps in knowledge. The
relationship between the bank macrophyte taxa and some of the hydromorphological
characteristics of the site may give weight to the suggestion of Kelly-Quinn et al. (2005) that
bank macrophyte taxa might be important for the assessment of hydromorphology. However, it
might also be the case that riparian vegetation is better related to physico-chemical conditions
of higher flow events, during which such species would be under the direct influence of the
water column (Vervuren et al. 2003). If variation in the bank macrophyte community is
structured by such events then the monthly sampling carried out in this study which may
represent low flow conditions during the sampling periods (Demars & Harper 2005), may not
be able to explain their variation. Further work could be carried out to relate the characteristics
of high flow water quality parameters to the bank macrophyte community. The relationship
between bank macrophyte communities and the hydromorphological character of rivers could
be investigated by recording bank species during surveys of aquatic species. Such data could
then be compared with River Habitat Survey data being collected by the EPA for relationships
between the bank species and hydromorphological features on a wider scale and along a
gradient of hydromorphological pressure.
The paucity of studies on any facet of stream bed sediment chemistry in Ireland highlights the
lack of knowledge on Irish sediment characteristics. A project has begun in the border region
which may begin to address this issue by sampling stream bed sediment in Northern Ireland and
the border counties of the South (http://www.bgs.ac.uk/gsni/tellus). The potential for sediment
in the Milltown Lake catchment to act as a buffer against future inputs of P should be
investigated with regard to the distance required for a given amount of nutrient to be
assimilated. As this may be affected by flow, the presence of macrophytes and the substrate
type (Wilcock et al. 2004; Jarvie et al. 2005; Cotton et al. 2006), this potential may not be
uniform across the catchment and so detailed studies may be required to confirm it. To assess
the relationship between macrophyte community composition and sediment characteristics
samples of sediment should be taken from beneath macrophyte stands and the macrophyte
species should be recorded. Collection of samples from across a wide range of rivers and
species would help to provide an indication of species sediment preferences. Such work should
197
also include physico-chemical sampling of the water column as certain species have been
shown to exhibit preferences for particular sediment-water nutrient balances (Schneider &
Melzer 2004).
Further work is also required to assess the relationships between species and nutrient
concentrations in Ireland to ensure that species lists and trophic rankings in macrophyte indices
reflect species optima along a gradient of impact and at a wide range of physical types. Future
use of the MTR in any catchment study or ‗eutrophication audit‘ should include a number of
sites from each river type so that sites can be judged relative to one another. Use of data from
sites along a wider gradient of impact in each river type might also help to place scores into
context.
Although the number of reference sites from within the national monitoring program was
exhausted in the formation of the typologies, reference condition sites may exist outside of the
program. Reference sites for all river types must be sought to give confidence to the
classifications currently in use. However, empirical modelling of water quality and its drivers
across the country could suggest areas which may provide reference condition sites based on
e.g. low percentage of agricultural land use (Donohue et al. 2006) or livestock numbers (Irvine
et al. 2002). These particular conditions could be located in new catchments by Geographical
Information System (GIS) based analyses. Site characteristics could then be assessed by
conducting surveys on rivers which meet the GIS criteria. Although labour intensive, finding
additional reference sites would add confidence not only to the river typology, but also to the
assessments of water quality made using type specific tools.
198
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Appendices
224
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, June 2006
Species Name D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3
Alisma plantago aquatica L. 0 0 0 0 0 0 0 0 0 0 0 0
Angelica sylvestris L. 0 0 1 1 0 0 0 0 0 1 1 2
Brachythecium rivulare Schimp. 0 0 1 0 0 0 2 1 1 1 1 1
Callitriche obtusangula Le Gall 1 0 2 0 1 0 1 0 0 1 1 1
Caltha palustris L. 0 1 0 0 0 0 1 1 1 0 0 0
Carex spp. 0 0 0 0 0 0 0 0 0 0 0 1
Chiloscyphus polyanthos (L.) Dumort 0 1 1 0 1 2 2 0 0 0 0 8
Elodea canadensis Michx 0 0 2 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 1 0 0 0 0 0 0 1 1 0
Filamentous algae 1 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 1 1 2 3 1 0 0 0 1 1 2 2
Fissidens spp. 5 2 1 1 0 0 0 1 0 0 0 0
Fontinalis antipyretica Hedw. 2 1 0 1 3 2 4 1 2 1 1 1
Glyceria maxima (Hartm.) Holmberg 0 0 2 2 0 0 0 0 0 0 3 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 3 0 0 0 0 0 0 1 1 0 1 0
Hygrohypnum luridum (Hedw.) Jenn 0 0 0 0 0 1 0 0 0 2 1 0
Iris pseudacorus L. 0 0 0 4 0 0 1 0 0 0 0 0
Juncus effusus L. 1 1 2 3 1 0 2 0 0 1 1 2
Lemanea fluviatilis L. 0 0 0 0 0 1 0 0 0 0 1 0
Lemna minor L. 1 0 3 0 0 0 0 0 0 1 0 1
Leptodictium riparium (Hedw.) Warnst 1 1 2 1 0 0 1 0 0 1 0 0
Lunularia cruciata (L.) 0 1 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 0 0 0 0 0 0 0 0 0 1 0 0
Oenanthe crocata L. 1 4 0 0 2 1 7 0 3 0 0 0
Pellia endiviifolia (Dicks.) Dumort 0 3 1 0 1 1 1 1 1 0 1 0
Persicaria hydropiper L. 0 0 0 0 0 0 0 0 0 1 0 0
Phalaris arundinacea L. 1 1 2 1 2 1 2 0 2 2 3 0
Platyhypnidium riparioides (Hedw.) Dixon 1 2 1 1 3 1 0 0 1 0 0 0
Ranunculus flammula L. 0 0 1 2 0 1 0 0 0 2 2 2
Rorippa nasturtium aquaticum agg. 1 0 1 1 0 0 0 0 0 5 0 1
Sparganium emersum Rehm. 1 0 0 0 0 0 0 0 0 0 0 0
Stachys spp. 0 0 0 0 0 0 0 0 0 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 3 0 0 0 0 0 0
Typha latifolia L. 0 0 2 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 0 0 1 1 1 0 0 0 0 1 1 1
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 0 0 0
Sampling Date 06 06 09 08 08 08 07 07 07 09 09 16
MTR Suffix of Confidence b c b c c c c c c b c b
225
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, June 2006 Contd.
Species Name GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2 CN1
Alisma plantago aquatica L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Angelica sylvestris L. 1 3 1 1 1 1 1 1 1 1 1 1 1
Brachythecium rivulare Schimp. 0 1 0 0 1 0 3 0 0 5 1 0 1
Callitriche obtusangula Le Gall 2 2 1 2 0 1 2 0 0 0 0 0 4
Caltha palustris L. 1 0 2 1 1 1 1 0 0 0 1 0 3
Carex spp. 0 1 0 0 1 0 0 1 1 0 0 0 0
Chiloscyphus polyanthos (L.) Dumort 0 1 0 3 1 0 3 1 0 6 0 0 1
Elodea canadensis Michx 0 0 0 0 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 2 1 0 2 0 1 1 1 0 0 3
Filamentous algae 0 2 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 2 2 2 0 1 1 1 2 1 1 1 2 4
Fissidens spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Fontinalis antipyretica Hedw. 0 1 0 1 1 1 2 1 0 3 1 1 6
Glyceria maxima (Hartm.) Holmberg 0 1 0 0 0 0 0 0 0 0 0 0 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 1 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 1 1 0 1 0 0 0 0 0 0 1 0 0
Hygrohypnum luridum (Hedw.) Jenn 0 1 0 0 0 0 0 0 0 0 0 0 0
Iris pseudacorus L. 0 2 0 4 1 0 0 0 0 0 0 0 0
Juncus effusus L. 1 4 1 1 1 1 1 1 1 1 1 1 2
Lemanea fluviatilis L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Lemna minor L. 1 0 5 2 1 1 1 1 0 0 0 0 0
Leptodictium riparium (Hedw.) Warnst 0 0 0 1 0 0 0 1 0 0 0 1 0
Lunularia cruciata (L.) 2 0 0 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 1 1 1 1 0 1 1 1 1 0 0 0 0
Oenanthe crocata L. 3 1 0 0 0 0 0 0 0 0 0 0 0
Pellia endiviifolia (Dicks.) Dumort 3 1 0 2 2 0 1 0 1 2 1 0 0
Persicaria hydropiper L. 1 0 0 0 0 0 0 1 0 0 0 0 0
Phalaris arundinacea L. 0 1 1 2 0 0 6 5 1 0 0 0 2
Platyhypnidium riparioides (Hedw.) Dixon 0 0 0 1 0 1 3 1 0 0 1 1 6
Ranunculus flammula L. 0 3 3 1 1 0 0 2 1 1 1 1 2
Rorippa nasturtium aquaticum agg. 0 2 2 1 2 1 3 1 3 0 1 2 3
Sparganium emersum Rehm. 1 1 2 0 0 0 0 0 1 1 0 0 0
Stachys spp. 0 2 0 0 0 0 0 0 1 1 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 0 0 0 0 0 1 0 0
Typha latifolia L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 2 1 2 1 1 1 2 1 1 1 0 1 1
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 1 0 0 3
Sampling Date 16 16 12 12 12 13 13 06 14 14 09 09 09
MTR Suffix of Confidence c b c b c b b b c c c c b
226
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, September 2006
Species Name D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3 GO4
Alisma plantago aquatica L. 1 0 0 0 0 0 0 0 0 0 0 0 0
Angelica sylvestris L. 0 0 1 1 0 0 0 0 0 1 2 3 3
Brachythecium rivulare Schimp. 0 0 1 0 0 0 2 1 2 1 1 1 1
Callitriche obtusangula Le Gall 1 0 2 0 1 0 3 0 0 1 1 2 0
Caltha palustris L. 0 1 0 0 0 0 1 1 1 0 0 0 2
Carex spp. 0 0 0 0 0 0 0 0 0 0 0 1 0
Chiloscyphus polyanthos (L.) Dumort 0 1 2 0 1 2 4 0 0 0 0 8 3
Elodea canadensis Michx 0 0 2 0 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 1 1 0 0 0 0 0 1 1 0 1
Filamentous algae 0 0 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 1 1 2 2 1 0 0 0 1 1 2 2 3
Fissidens spp. 6 2 1 1 0 0 0 1 0 0 0 0 0
Fontinalis antipyretica Hedw. 2 1 0 2 3 3 5 1 3 1 2 2 2
Glyceria maxima (Hartm.) Holmberg 0 0 2 2 0 0 0 0 0 0 3 0 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 2 0 0 0 0 0 0 1 1 0 1 0 0
Hygrohypnum luridum (Hedw.) Jenn 0 0 0 0 0 1 0 0 0 2 1 0 0
Iris pseudacorus L. 0 0 0 4 0 0 1 0 0 0 0 0 0
Juncus effusus L. 1 2 2 3 1 0 2 0 1 2 2 3 2
Lemanea fluviatilis L. 0 0 0 0 0 1 0 0 0 0 1 0 0
Lemna minor L. 1 0 3 0 1 0 0 0 0 1 1 1 0
Leptodictium riparium (Hedw.) Warnst 2 1 2 2 0 0 1 0 0 1 0 0 0
Lunularia cruciata (L.) 0 1 0 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 0 0 0 0 0 0 0 0 0 1 0 0 1
Oenanthe crocata L. 1 4 0 0 3 2 7 0 3 0 0 0 0
Pellia endiviifolia (Dicks.) Dumort 0 3 1 0 1 1 1 1 1 0 1 0 0
Persicaria hydropiper L. 0 0 0 0 0 0 0 0 0 1 0 0 0
Phalaris arundinacea L. 2 1 3 2 4 1 3 0 3 2 1 0 0
Platyhypnidium riparioides (Hedw.) Dixon 1 2 2 1 3 1 0 0 1 0 0 0 0
Ranunculus flammula L. 0 0 2 3 0 1 0 0 0 1 2 3 4
Rorippa nasturtium aquaticum agg. 2 0 1 2 0 0 0 0 0 5 1 2 3
Sparganium emersum Rehm. 1 1 1 0 0 0 1 0 0 1 0 0 0
Stachys spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 2 0 0 0 0 0 0 0
Typha latifolia L. 0 0 2 0 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 0 0 1 1 1 0 0 0 0 1 1 1 0
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 0 0 0 1
Sampling Date 11 11 13 12 12 14 14 14 11 13 19 19 19
MTR Suffix of Confidence b b a c b c b c c b b b c
227
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, September 2006 Contd.
Species Name GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2 CN1
Alisma plantago aquatica L. 0 0 0 0 0 1 0 0 0 0 0 0 0
Angelica sylvestris L. 1 3 2 2 1 1 1 1 2 2 1 1 2
Brachythecium rivulare Schimp. 0 1 0 0 1 0 3 0 0 5 1 0 1
Callitriche obtusangula Le Gall 1 2 3 1 0 1 2 0 0 0 0 0 4
Caltha palustris L. 1 0 2 1 1 1 1 0 0 0 1 0 3
Carex spp. 0 1 0 0 1 0 0 1 1 0 0 0 0
Chiloscyphus polyanthos (L.) Dumort 0 1 0 3 1 0 3 1 0 5 0 0 1
Elodea canadensis Michx 0 0 0 0 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 2 1 0 1 0 1 2 2 0 0 2
Filamentous algae 0 0 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 1 2 1 0 1 1 1 1 1 1 1 1 4
Fissidens spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Fontinalis antipyretica Hedw. 0 1 0 1 2 2 2 2 0 3 1 1 6
Glyceria maxima (Hartm.) Holmberg 0 1 0 0 0 0 0 0 0 0 0 0 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 3 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 1 2 0 1 0 0 0 0 0 0 2 0 0
Hygrohypnum luridum (Hedw.) Jenn 0 1 0 0 0 0 0 0 0 0 0 0 0
Iris pseudacorus L. 0 2 0 3 1 0 0 0 0 0 0 0 0
Juncus effusus L. 2 2 2 2 1 1 2 1 2 1 1 3 3
Lemanea fluviatilis L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Lemna minor L. 1 0 4 1 1 1 1 1 0 0 0 0 0
Leptodictium riparium (Hedw.) Warnst 0 0 0 1 0 0 0 1 0 0 0 1 0
Lunularia cruciata (L.) 2 0 0 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 1 3 2 1 0 1 1 1 1 0 0 0 0
Oenanthe crocata L. 3 1 0 0 0 0 0 0 0 0 0 0 0
Pellia endiviifolia (Dicks.) Dumort 1 1 0 2 2 0 1 0 1 2 1 0 0
Persicaria hydropiper L. 1 0 0 0 0 0 0 1 0 0 0 0 0
Phalaris arundinacea L. 0 1 2 2 0 0 6 5 2 0 0 0 2
Platyhypnidium riparioides (Hedw.) Dixon 0 0 0 1 0 2 3 1 0 0 1 2 5
Ranunculus flammula L. 0 4 1 2 1 0 0 2 1 2 1 1 2
Rorippa nasturtium aquaticum agg. 0 3 3 1 2 1 3 1 4 1 1 1 3
Sparganium emersum Rehm. 1 1 3 0 0 0 0 0 2 1 0 0 0
Stachys spp. 0 2 0 0 0 0 0 0 1 1 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 0 0 0 0 0 2 0 0
Typha latifolia L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 1 1 1 1 1 1 2 1 1 1 0 1 1
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 1 0 0 3
Sampling Date 20 20 13 22 22 22 21 20 21 21 15 15 15
MTR Suffix of Confidence c b c b c b b b c c c c b
228
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, June 2007
Species Name D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3 GO4
Alisma plantago aquatica L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Angelica sylvestris L. 0 0 1 1 0 0 0 0 0 1 1 1 1
Brachythecium rivulare Schimp. 0 0 1 0 0 0 2 1 2 1 2 1 2
Callitriche obtusangula Le Gall 0 0 4 0 0 0 0 0 0 0 1 1 0
Caltha palustris L. 0 1 2 0 0 0 1 1 1 0 0 0 1
Carex spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Chiloscyphus polyanthos (L.) Dumort 0 2 1 0 2 2 4 0 0 0 0 8 1
Elodea canadensis Michx 0 0 3 0 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 2 1 0 0 0 0 0 1 1 0 1
Filamentous algae 0 0 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 1 1 0 2 1 0 0 0 1 1 1 1 2
Fissidens spp. 5 2 0 1 0 0 0 1 0 0 0 0 0
Fontinalis antipyretica Hedw. 1 1 0 1 2 1 4 1 3 1 2 2 1
Glyceria maxima (Hartm.) Holmberg 0 0 0 1 0 0 0 0 0 0 1 0 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 2 0 0 0 0 0 0 2 1 0 2 0 0
Hygrohypnum luridum (Hedw.) Jenn 0 0 0 0 0 1 0 0 0 2 1 0 0
Iris pseudacorus L. 0 0 0 3 0 0 1 0 0 0 0 0 0
Juncus effusus L. 1 1 4 3 2 0 2 0 2 1 2 2 2
Lemanea fluviatilis L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Lemna minor L. 0 1 5 0 0 0 0 0 0 1 1 0 0
Leptodictium riparium (Hedw.) Warnst 2 1 2 1 0 0 1 0 0 1 0 0 0
Lunularia cruciata (L.) 0 1 0 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 0 0 1 0 0 0 0 0 0 0 1 0 0
Oenanthe crocata L. 2 4 0 0 4 1 6 0 4 0 0 0 0
Pellia endiviifolia (Dicks.) Dumort 0 3 1 0 1 2 1 1 2 0 2 0 0
Persicaria hydropiper L. 0 0 0 0 0 0 0 0 0 1 0 0 0
Phalaris arundinacea L. 3 1 3 2 4 1 1 0 3 1 3 0 0
Platyhypnidium riparioides (Hedw.) Dixon 2 2 2 1 2 2 0 0 2 0 0 0 0
Ranunculus flammula L. 0 0 3 2 0 1 0 0 0 1 2 3 5
Rorippa nasturtium aquaticum agg. 1 1 0 2 0 0 0 0 0 3 1 1 1
Sparganium emersum Rehm. 1 0 3 0 0 0 0 0 0 0 0 0 0
Stachys spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 2 0 0 0 0 0 0 0
Typha latifolia L. 0 0 2 0 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 0 0 3 1 1 0 0 0 0 0 1 1 0
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 0 0 0 1
Sampling Date 25 25 27 27 27 26 26 26 25 28 28 28 29
MTR Suffix of Confidence c b c c c c c c c b b c c
229
Appendix 1 Macrophyte abundance classes for the Milltown Lake catchment, June 2007 Contd.
Species Name GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2 CN1
Alisma plantago aquatica L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Angelica sylvestris L. 1 1 1 1 1 1 1 1 1 1 1 1 2
Brachythecium rivulare Schimp. 0 1 0 0 1 0 3 0 0 5 2 0 1
Callitriche obtusangula Le Gall 0 2 3 3 0 0 1 1 0 0 0 0 1
Caltha palustris L. 1 0 2 2 1 1 1 0 0 0 1 0 2
Carex spp. 0 0 0 0 1 0 0 1 1 0 0 0 0
Chiloscyphus polyanthos (L.) Dumort 0 1 0 4 2 0 3 2 0 5 0 0 1
Elodea canadensis Michx 0 0 0 0 0 0 0 0 0 0 0 0 0
Equisetum arvense L. 0 0 2 1 0 1 0 1 2 1 0 0 1
Filamentous algae 0 0 0 0 0 0 0 0 0 0 0 0 0
Filipendula ulmaria (L.) Maxim. 1 1 1 0 1 1 1 1 1 1 1 1 3
Fissidens spp. 0 0 0 0 0 0 0 0 0 0 0 0 0
Fontinalis antipyretica Hedw. 0 1 0 2 1 1 2 1 0 3 3 1 6
Glyceria maxima (Hartm.) Holmberg 0 1 0 0 0 0 0 0 0 0 0 0 0
Hydrocotyle vulgaris L. 0 0 0 0 0 0 0 0 0 1 0 0 0
Hygroambylestegium fluviatile (Hedw.) Loeske 3 2 0 3 0 0 0 0 0 0 3 0 0
Hygrohypnum luridum (Hedw.) Jenn 0 1 0 0 0 0 0 0 0 0 0 0 0
Iris pseudacorus L. 0 2 0 4 1 0 0 0 0 0 0 0 0
Juncus effusus L. 1 1 1 3 2 3 3 2 1 2 2 1 4
Lemanea fluviatilis L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Lemna minor L. 1 0 3 1 0 0 0 0 0 0 0 0 0
Leptodictium riparium (Hedw.) Warnst 0 0 0 1 0 0 0 1 0 0 0 1 0
Lunularia cruciata (L.) 1 0 0 0 0 0 0 0 0 0 0 0 0
Myosotis spp. 1 1 2 1 0 1 1 1 2 0 0 0 0
Oenanthe crocata L. 3 1 0 0 0 0 0 0 0 0 0 0 0
Pellia endiviifolia (Dicks.) Dumort 3 1 0 3 2 0 2 0 1 2 1 0 0
Persicaria hydropiper L. 1 0 0 0 0 0 0 1 0 0 0 0 0
Phalaris arundinacea L. 0 1 3 3 0 0 5 5 2 0 0 0 3
Platyhypnidium riparioides (Hedw.) Dixon 0 0 0 1 0 3 3 1 0 0 3 1 4
Ranunculus flammula L. 0 3 2 1 2 0 0 1 2 2 1 1 5
Rorippa nasturtium aquaticum agg. 0 2 3 1 2 2 3 2 3 0 1 1 1
Sparganium emersum Rehm. 0 1 2 0 0 0 0 0 0 0 0 0 0
Stachys spp. 0 0 0 0 0 0 0 0 1 1 0 0 0
Thamnobryum alopecurum (Hedw.) Gangulee 0 0 0 0 0 0 0 0 0 0 1 0 0
Typha latifolia L. 0 0 0 0 0 0 0 0 0 0 0 0 0
Veronica anagallis-aquatica L. 1 1 1 1 1 1 2 0 1 1 0 1 2
Veronica beccabunga L. 0 0 0 0 0 0 0 0 0 0 0 0 2
Sampling Date 29 29 12 13 13 13 14 14 15 15 12 12 12
MTR Suffix of Comparability c b c b c c b b c c c c b
230
Appendix 1 Matrix of suffices of similarity for comparison of macrophyte surveys (Holmes 1999)
Site D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3 GO4 GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2
D1
D2 II
TV1 II I
TV2 II II I
TV3 II I I I
TV4 II I I I I
TV5 II I I I I I
TV6 II I I I I I I
TV7 II III II II II II II II
GO1 III II II II I II II II II
GO2 II I I I I I I I II II
GO3 II II I I I I I II II II II
GO4 II I I I I I I I II II I II
GO5 III II I I I I I I I I I II I
GO6 II II II II I I I II II I II I II II
TH1 III II I I I I I I I I I I I I II
TH2 II II I I I I I I I I I I I I I I
TH3 III II I II I I I I II I I II II I II I I
TH4 III II I I I I I I II II I I I I I I I I
TH5 II I I I I I I I II II I I II I I I I I I
TH6 III II I I I I I I II II I I I I I I I I I I
MA2 II I II II II II II II II I II II II II I II II II II II II
MA4 III II I I I I I II III II I I I I II I II I I II I III
DG1 II I I I I I I I II I I II I I II I I I I I I II I
DG2 II II II II I I I II I I II II II I I I I I II I II I II II
CN1 III II II I I I I II II II II I II I II I I I II I II II I II I
231
Appendix 2 Water column physico-chemical parameters for the Milltown Lake catchment, May 2006
Site Date TRP
mg/L P
NH4
mg/L N
DO
mg/L O2
BOD
mg/L O2
Conductivity
µS/cm
pH Alkalinity
mg/L CaCO3
Temperature
ºC
D1 09/05/2006 0.018 0.06 10.66 4.78 143.1 7.50 70 10.8
D2 09/05/2006 0.015 0.05 11.50 3.08 237.0 8.15 64 11.1
TV1 03/05/2006 0.018 0.08 7.13 7.88 121.4 7.04 64 10.2
TV2 03/05/2006 0.010 0.05 7.23 3.18 146.5 7.36 64 11.2
TV3 03/05/2006 0.011 0.05 7.52 1.99 167.2 7.82 80 8.7
TV4 03/05/2006 0.021 0.20 9.70 3.38 248.0 7.52 74 14.4
TV5 09/05/2006 0.021 0.04 10.33 3.28 252.0 7.45 74 12.6
TV6 09/05/2006 0.017 0.06 10.44 3.08 246.0 7.71 72 12.7
TV7 09/05/2006 0.014 0.04 11.16 5.37 260.0 7.82 76 11.5
GO1 03/05/2006 0.005 0.13 8.66 2.59 174.4 7.70 62 10.6
GO2 10/05/2006 0.010 0.03 8.63 <1.50 211.0 7.60 50 19.3
GO3 10/05/2006 0.015 0.03 10.63 3.88 194.0 8.76 48 18.1
GO4 10/05/2006 0.014 0.03 11.00 4.16 209.0 7.82 54 16.6
GO5 10/05/2006 0.027 0.04 9.55 4.08 226.0 7.66 56 13.9
GO6 10/05/2006 0.024 0.03 15.85 8.26 211.0 8.45 62 12.0
TH1 11/05/2006 0.001 0.14 9.75 3.58 178.0 7.31 48 16.7
TH2 11/05/2006 0.011 0.03 10.70 3.78 201.0 7.75 50 16.8
TH3 11/05/2006 0.009 0.03 11.78 3.48 215.0 7.47 52 15.2
TH4 11/05/2006 0.048 0.03 14.00 6.57 207.0 8.63 56 13.3
TH5 04/05/2006 0.059 0.03 10.25 4.16 205.0 7.32 56 13
TH6 10/05/2006 0.027 0.03 9.81 5.29 235.0 7.48 54 12.2
MA2 04/05/2006 0.018 0.23 7.38 4.16 131.5 7.42 42 9.0
MA4 04/05/2006 0.018 0.19 7.78 1.59 136.0 7.07 44 9.8
DG1 04/05/2006 0.018 0.08 6.80 4.16 109.8 7.54 48 9.0
DG2 04/05/2006 0.010 0.05 7.03 1.89 115.6 7.72 56 9.2
CN1 04/05/2006 0.011 0.05 9.43 4.16 149.3 7.22 84 8.2
232
Appendix 2 Water column physico-chemical parameters for the Milltown Lake catchment, October 2006
Site Date TRP
mg/L P
NH4
mg/L N
DO
mg/L O2
BOD
mg/L O2
Conductivity
µS/cm
pH Alkalinity
mg/L CaCO3
Temperature
ºC
D1 10/10/2006 0.420 0.030 8.01 2.25 135.4 6.82 60 11.9
D2 10/10/2006 0.092 0.020 8.13 2.85 137.6 6.95 54 11.8
TV1 03/10/2006 0.079 0.061 4.45 3.48 224.0 6.85 44 12.9
TV2 03/10/2006 0.074 0.042 8.37 2.29 235.0 6.95 52 12.5
TV3 03/10/2006 0.089 0.021 8.54 1.99 158.8 6.95 68 12.6
TV4 03/10/2006 0.065 0.040 8.1 <1.50 226.0 6.90 62 12.7
TV5 09/10/2006 0.379 0.046 7.71 3.65 145.4 7.28 48 11.3
TV6 09/10/2006 0.272 0.047 8.02 1.95 146.4 7.37 60 11.5
TV7 09/10/2006 0.426 0.074 8.41 3.95 229.0 7.28 68 12.1
GO1 04/10/2006 0.100 0.046 8.45 <1.50 193.2 6.84 46 11.0
GO2 09/10/2006 0.284 0.032 7.83 <1.50 119.1 6.87 36 11.9
GO3 09/10/2006 0.447 0.027 8.26 3.35 110.9 7.16 28 10.5
GO4 09/10/2006 0.190 0.021 8.57 2.05 116.8 7.31 18 12.8
GO5 09/10/2006 0.424 0.022 8.19 3.95 192.7 7.26 68 12.6
GO6 09/10/2006 0.080 0.019 8.27 1.75 128.8 7.19 80 12.4
TH1 04/10/2006 0.077 0.030 6.88 1.59 161.3 6.83 48 12.0
TH2 10/10/2006 0.554 0.021 7.98 3.35 99.5 6.90 44 11.0
TH3 10/10/2006 0.381 0.024 8.12 <1.50 165.7 7.02 24 12.4
TH4 10/10/2006 0.639 0.064 8.26 3.65 122.9 6.93 22 11.7
TH5 10/10/2006 0.372 0.046 8.33 2.12 186.8 7.16 28 10.7
TH6 10/10/2006 0.505 0.026 8.47 2.12 195.4 7.20 44 10.2
MA2 04/10/2006 0.058 - 8.71 3.38 176.0 7.07 26 11.1
MA4 04/10/2006 0.015 - 8.39 <1.50 165.6 7.09 44 12.0
DG1 04/10/2006 0.009 0.025 8.65 <1.50 174.4 7.04 42 12.5
DG2 04/10/2006 0.140 0.015 8.72 <1.50 174.9 6.95 54 12.2
CN1 04/10/2006 0.053 0.006 6.21 <1.50 223.0 6.82 76 11.5
233
Appendix 2 Water column physico-chemical parameters for the Milltown Lake catchment, May 2007
Site Date TRP
mg/L P
NH4
mg/L N
DO
mg/L O2
BOD
mg/L O2
Conductivity
µS/cm
pH Alkalinity
mg/L CaCO3
Temperature
ºC
D1 01/05/2007 0.032 0.090 11.14 3.78 142.4 8.19 64 12.2
D2 31/04/2007 0.069 0.127 11.52 1.50 145.0 8.25 68 15.9
TV1 08/05/2007 0.033 0.030 5.67 2.78 222.0 7.25 34 13.9
TV2 08/05/2007 0.094 0.019 9.85 1.89 145.7 8.01 54 10.3
TV3 09/05/2007 0.314 0.787 9.85 2.98 164.2 8.10 76 12.4
TV4 09/05/2007 0.081 0.475 9.45 2.09 164.6 7.02 88 13.4
TV5 31/04/2007 0.087 0.095 9.98 3.88 155.9 7.01 68 12.4
TV6 31/04/2007 0.102 0.101 9.96 1.59 148.3 8.20 66 14.0
TV7 01/05/2007 0.095 0.037 10.04 <1.5 161.8 8.01 76 13.0
GO1 09/05/2007 0.090 0.370 6.47 2.89 193.1 7.87 88 14.3
GO2 01/05/2007 0.070 0.161 9.34 2.49 144.4 6.88 48 15.4
GO3 01/05/2007 0.038 0.077 9.44 3.28 215.0 8.04 54 18.2
GO4 01/05/2007 0.029 0.041 9.26 <1.50 126.9 7.95 60 16.3
GO5 01/05/2007 0.047 0.225 9.43 2.09 126.9 7.62 78 15.1
GO6 01/05/2007 0.048 0.041 9.68 3.08 142.8 8.00 70 14.7
TH1 09/05/2007 0.061 0.072 8.06 2.36 128.4 7.76 62 12.6
TH2 02/05/2007 0.055 0.107 7.84 3.78 113.8 7.58 74 17.4
TH3 02/05/2007 0.053 0.038 9.51 3.98 131.2 8.14 62 17.8
TH4 02/05/2007 0.051 0.524 11.83 3.48 137.5 7.90 52 16.5
TH5 02/05/2007 0.056 0.181 9.05 3.88 126.2 8.61 54 14.2
TH6 01/05/2007 0.054 0.215 8.22 2.69 129.3 8.86 54 16.4
MA2 10/05/2007 0.144 0.152 10.45 <1.50 111.1 7.84 48 10.7
MA4 10/05/2007 0.027 0.107 10.85 <1.50 115.3 8.10 48 9.6
DG1 09/05/2007 0.065 0.258 10.10 4.13 105.2 7.94 44 13.7
DG2 09/05/2007 0.102 0.115 9.75 2.39 114.1 7.86 50 13.0
CN1 08/05/2007 0.037 0.106 9.25 2.88 157.3 7.82 84 12.4
234
Appendix 2 Monthly Conductivity µS/cm data for the Milltown Lake catchment provided by the National Source Protection Pilot
Project
Site Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 May-07 June-07 Median
D2 364.00 241.00 137.60 198.00 193.40 217.00 208.00 113.00 145.00 146.80 195.70
TV1 220.00 171.70 224.00 211.00 197.20 195.20 147.30 120.40 222.00 235.00 204.10
TV2 353.00 233.00 235.00 201.00 209.00 216.00 226.00 125.40 147.50 140.20 205.00
TV3 304.00 301.00 158.80 196.60 137.90 105.00 144.90 233.00 164.20 164.20 164.20
TV4 342.00 186.10 226.00 138.20 130.00 138.70 225.00 211.00 164.60 154.30 175.35
TV5 223.00 184.60 145.40 137.90 128.30 121.60 145.70 136.70 155.90 285.00 145.55
TV6 240.00 283.00 146.40 148.20 127.90 141.00 145.60 213.00 148.30 154.30 148.25
TV7 300.00 280.00 229.00 217.00 133.40 222.00 231.00 121.50 161.30 161.90 219.50
GO1 259.00 259.00 193.20 106.00 171.00 196.00 188.50 106.60 193.10 149.30 190.80
GO2 314.00 142.70 119.10 163.10 109.00 123.70 118.20 81.30 144.40 129.70 126.70
GO3 192.60 209.00 110.90 100.90 88.80 81.30 173.80 111.90 215.00 248.00 142.85
GO4 319.00 214.00 116.80 105.80 170.10 126.20 182.30 185.60 126.90 132.10 151.10
GO5 343.00 153.30 192.70 114.40 180.00 132.30 122.10 197.00 133.80 126.90 143.55
GO6 347.00 239.00 128.80 182.60 189.50 134.90 130.70 111.90 142.80 123.30 138.85
TH1 266.00 174.20 161.30 150.20 63.50 162.40 98.50 85.80 124.80 95.60 137.50
TH2 173.60 108.10 99.50 92.10 83.60 104.70 144.60 147.70 113.80 98.20 106.40
TH3 197.90 125.10 165.70 101.90 103.10 119.50 111.00 168.50 125.60 131.20 125.35
TH4 310.00 135.60 122.90 172.30 233.00 110.10 100.30 180.30 137.50 129.70 136.55
TH5 203.00 205.00 186.80 173.60 172.70 125.70 82.30 105.70 126.20 225.00 173.15
TH6 340.00 215.00 195.40 116.10 118.60 131.40 120.70 190.80 129.30 238.00 161.10
MA2 311.00 140.10 176.00 114.10 106.80 122.70 80.30 123.40 142.10 111.10 123.05
MA4 184.30 113.70 165.60 162.30 157.30 115.70 161.90 173.20 115.30 113.10 159.60
DG1 203.00 219.00 174.40 164.10 147.80 87.70 162.30 83.00 105.20 113.20 155.05
DG2 239.00 222.00 174.90 178.90 153.40 166.30 75.40 156.90 109.60 114.10 161.60
CN1 380.00 286.00 223.00 133.30 128.60 95.80 140.90 137.40 157.30 146.00 143.45
235
Appendix 2 Monthly water column TDS g/L data for the Milltown Lake catchment provided by the National Source Protection Pilot
Project
Site Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Median
D2 218.00 148.10 83.00 118.60 115.80 130.40 125.90 68.00 85.70 88.10 117.20
TV1 133.30 103.30 134.10 127.50 118.00 117.20 88.70 72.30 78.30 141.10 117.60
TV2 216.00 233.00 140.90 119.10 125.50 129.20 137.00 75.10 88.50 84.30 122.30
TV3 182.00 181.60 93.80 116.30 92.40 59.90 88.60 139.90 98.50 98.50 98.50
TV4 205.00 111.50 135.70 83.10 78.00 82.90 135.70 126.50 98.80 92.80 105.15
TV5 137.40 111.60 87.60 82.90 77.50 73.10 88.70 81.50 93.60 171.40 88.15
TV6 143.80 145.20 87.80 81.80 76.90 84.60 87.50 127.50 93.50 95.90 90.65
TV7 150.80 172.60 137.60 129.80 80.60 133.20 139.90 72.80 96.80 97.20 131.50
GO1 206.00 155.70 115.70 63.70 102.50 117.70 83.90 63.90 115.70 89.30 109.10
GO2 189.00 83.60 71.20 98.20 65.40 74.50 71.30 48.60 86.70 78.00 76.25
GO3 110.90 126.40 66.50 61.00 53.20 48.80 104.90 67.10 129.20 119.70 86.00
GO4 191.20 126.10 70.50 63.60 102.00 75.70 108.70 111.30 76.60 79.30 90.65
GO5 206.00 92.40 115.60 68.10 106.50 79.40 74.20 118.20 80.10 76.70 86.25
GO6 209.00 157.30 77.30 109.90 113.40 80.90 78.40 67.10 85.70 73.90 83.30
TH1 159.90 104.50 96.80 90.40 38.10 97.50 58.60 50.90 74.90 58.30 82.65
TH2 156.10 65.50 59.70 55.00 43.50 62.80 87.90 88.50 68.40 57.90 64.15
TH3 181.20 75.60 100.70 61.20 61.80 71.90 45.80 100.90 80.30 78.70 77.15
TH4 186.00 81.60 73.40 103.30 139.30 66.00 60.70 108.10 82.40 77.70 82.00
TH5 186.60 125.20 112.90 104.60 75.80 75.40 49.20 63.40 75.70 135.10 90.20
TH6 204.00 130.00 117.60 109.60 71.20 78.70 73.50 114.30 78.20 143.10 111.95
MA2 188.40 81.10 105.90 68.30 64.60 73.70 48.50 74.00 85.20 82.10 77.55
MA4 110.70 66.20 99.40 80.20 95.30 70.00 100.10 103.90 69.20 53.50 87.75
DG1 138.70 133.60 104.60 98.30 88.50 52.80 98.70 49.80 63.10 66.60 93.40
DG2 144.50 133.70 105.00 107.60 92.90 99.80 45.10 94.10 65.80 68.40 96.95
CN1 229.00 173.10 133.70 80.00 77.70 57.50 84.80 82.40 93.60 87.60 86.20
236
Appendix 2 Monthly water column pH data for the Milltown Lake catchment provided by the National Source Protection Pilot Project
Site Jun-06 Aug-06 Sep-06 Oct-06 Nov-06 Jan-07 Feb-07 Mar-07 May-07 June-07 Median
D2 7.73 7.93 7.61 6.95 7.41 7.16 7.61 7.75 8.25 8.32 7.67
TV1 6.99 7.19 5.21 6.85 7.38 7.03 7.30 7.28 7.25 7.18 7.19
TV2 7.59 7.63 7.51 6.95 7.36 6.98 7.68 7.65 8.01 7.66 7.61
TV3 7.61 7.73 7.00 6.95 7.37 7.03 7.77 7.79 8.10 7.60 7.61
TV4 7.50 7.56 7.14 6.90 7.34 7.00 7.60 7.61 7.92 7.60 7.53
TV5 7.53 7.23 7.66 7.28 7.42 7.02 7.62 7.63 7.01 7.63 7.48
TV6 7.86 7.87 7.57 7.37 7.33 7.06 7.85 7.85 8.20 7.92 7.85
TV7 7.82 7.93 7.44 7.28 7.34 7.12 7.77 7.88 8.01 7.90 7.80
GO1 7.57 7.47 6.86 6.84 7.39 7.05 7.44 7.69 7.87 7.91 7.46
GO2 7.54 7.21 7.43 6.87 7.44 7.03 7.55 7.53 6.88 7.64 7.44
GO3 6.86 7.52 7.23 7.16 7.42 7.08 7.74 7.69 8.04 7.84 7.47
GO4 7.58 7.52 7.60 7.31 7.39 7.04 7.89 7.86 7.95 7.90 7.59
GO5 6.48 7.21 7.49 7.26 7.36 6.99 7.70 7.62 7.82 7.58 7.43
GO6 7.71 7.77 7.56 7.19 7.40 6.98 7.67 7.70 8.00 7.77 7.69
TH1 7.06 7.11 7.67 6.38 7.39 7.03 7.41 7.39 7.76 7.46 7.39
TH2 7.60 7.08 6.92 6.90 7.43 7.25 7.81 7.69 7.58 7.64 7.51
TH3 7.32 8.02 7.45 7.02 7.37 7.23 7.89 7.90 8.14 8.14 7.67
TH4 7.25 7.44 7.36 6.93 7.42 7.18 7.80 7.72 7.90 8.09 7.43
TH5 7.44 7.55 7.36 7.16 7.42 7.18 7.76 7.79 8.51 8.11 7.50
TH6 7.37 7.69 7.43 7.20 7.35 7.11 7.72 7.78 8.86 8.01 7.56
MA2 7.01 7.53 7.22 7.07 7.40 6.96 7.84 7.84 8.22 7.89 7.47
MA4 7.86 7.51 0.00 7.09 7.42 6.98 7.78 7.76 8.10 7.78 7.64
DG1 7.51 7.45 6.64 7.04 7.41 6.81 7.76 7.71 7.94 7.79 7.48
DG2 7.69 7.44 6.70 6.95 7.30 6.79 7.84 7.86 8.29 7.86 7.57
CN1 7.67 7.59 7.47 6.82 7.36 6.78 7.47 7.47 7.82 7.44 7.47
237
Appendix 2 Monthly water column DO % SAT data for the Milltown Lake catchment provided by the National Source Protection
Pilot Project
Site Oct-05 May-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 May-07 Median
D2 56.30 114.40 78.90 101.40 83.70 75.80 103.90 89.00 113.40 117.40 95.20
TV1 41.80 73.50 98.00 33.20 53.80 42.90 69.20 75.00 82.80 62.40 65.80
TV2 - 93.60 - 75.60 - 82.20 98.90 99.40 120.60 100.60 98.90
TV3 60.90 89.00 58.90 71.80 76.30 82.80 96.40 101.80 136.10 97.20 85.90
TV4 56.60 84.50 91.20 69.80 62.50 78.60 99.00 102.80 113.30 217.00 87.85
TV5 66.80 102.80 70.10 69.60 74.80 73.00 101.40 92.70 118.50 94.20 83.75
TV6 79.50 106.20 85.50 91.30 84.20 75.10 102.60 95.50 149.10 98.30 93.40
TV7 88.40 108.20 81.00 70.60 94.20 78.80 103.80 93.80 101.60 96.30 94.00
GO1 51.50 92.50 73.50 78.20 83.60 82.80 126.30 91.10 122.90 143.00 87.35
GO2 54.00 97.10 80.70 76.40 81.50 74.60 112.10 94.00 115.30 95.20 87.75
GO3 55.50 93.10 73.80 78.20 92.70 79.50 111.40 94.10 117.10 102.10 92.90
GO4 57.00 94.80 72.30 86.50 92.30 81.70 103.20 97.50 115.20 95.50 93.55
GO5 48.10 102.40 72.60 73.40 74.30 78.30 99.00 95.80 109.70 101.70 87.05
GO6 52.50 89.30 79.90 80.70 80.50 78.30 99.70 89.90 117.70 97.00 85.00
TH1 30.30 100.80 20.30 52.60 67.30 66.10 85.40 82.50 - 162.10 67.30
TH2 62.40 88.40 61.20 79.10 81.70 74.00 98.70 104.20 - 83.40 81.70
TH3 68.90 92.40 - 83.20 85.70 76.00 105.60 103.70 - 101.80 89.05
TH4 69.80 89.90 66.70 82.00 100.30 76.40 115.30 105.50 - 122.00 89.90
TH5 68.70 92.60 69.80 79.60 81.20 76.20 103.10 95.40 122.80 89.40 85.30
TH6 70.50 91.50 70.40 81.60 78.10 76.20 101.40 70.40 114.40 84.60 79.85
MA2 56.00 87.70 64.30 79.00 86.90 81.70 108.50 95.40 - 241.00 86.90
MA4 75.10 91.00 65.20 81.10 70.70 80.10 99.70 97.00 - 186.00 81.10
DG1 67.70 87.20 15.90 74.20 95.20 84.30 99.90 105.10 127.40 102.00 91.20
DG2 68.00 88.20 78.20 72.10 78.40 84.20 115.10 110.10 119.30 108.80 86.20
CN1 49.50 80.50 49.00 65.40 75.50 58.70 89.30 92.10 112.10 97.70 78.00
238
Appendix 2 Monthly water column DO mg/L data for the Milltown Lake catchment provided by the National Source Protection Pilot
Project
Site Jul-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Median
D2 10.55 8.06 9.65 10.25 9.05 11.84 10.75 11.54 12.04 11.52 10.65
TV1 4.12 5.77 4.08 4.76 6.87 7.96 7.86 7.86 7.36 5.67 6.32
TV2 8.84 8.59 7.96 10.15 8.86 10.35 9.95 10.15 9.65 9.85 9.75
TV3 7.77 9.00 8.86 9.55 8.86 11.44 9.95 10.45 9.05 9.85 9.30
TV4 8.96 7.31 7.86 10.25 8.86 12.04 10.15 10.55 9.25 9.45 9.35
TV5 6.27 6.97 8.66 10.25 9.15 11.54 11.04 10.65 9.05 9.98 9.57
TV6 6.17 9.35 8.76 11.04 9.05 11.94 10.45 10.85 9.85 9.96 9.91
TV7 8.86 8.16 9.15 10.85 9.45 11.24 11.04 10.75 10.35 10.04 10.20
GO1 7.46 8.46 9.95 11.74 9.65 12.24 10.45 11.34 9.25 6.47 9.80
GO2 8.66 8.56 9.05 11.23 9.65 11.54 10.35 11.54 9.85 9.34 9.75
GO3 9.15 8.76 8.66 11.64 6.87 12.24 10.35 11.94 11.14 9.44 9.90
GO4 10.23 7.96 9.65 11.45 9.95 12.04 10.85 11.54 10.85 9.26 10.54
GO5 8.06 8.26 9.05 11.04 9.65 11.94 10.55 11.54 10.15 9.43 9.90
GO6 7.86 8.26 9.35 10.55 10.95 12.04 10.55 11.04 9.75 9.68 10.15
TH1 3.58 7.46 8.06 9.35 7.86 10.15 11.54 11.14 6.57 8.06 8.06
TH2 7.26 8.81 9.15 10.75 9.35 11.14 10.75 11.34 9.35 7.84 9.35
TH3 10.05 8.81 - 9.65 9.85 11.84 10.65 12.44 9.25 9.51 9.75
TH4 9.73 8.76 9.45 10.75 8.76 11.04 10.75 10.95 11.64 11.83 10.75
TH5 10.86 8.96 10.12 11.94 10.05 11.34 10.85 11.74 10.25 9.05 10.55
TH6 10.35 8.16 10.19 11.34 9.95 12.34 10.85 11.84 10.95 8.22 10.60
MA2 8.34 8.46 11.24 11.41 7.16 11.74 10.15 11.74 10.85 10.45 10.65
MA4 9.62 8.16 10.95 11.54 9.95 11.94 10.95 11.94 9.45 10.85 10.90
DG1 7.86 8.76 8.86 10.25 9.45 11.74 10.65 10.75 10.65 10.10 10.18
DG2 8.66 7.76 9.15 10.95 9.05 11.44 10.85 10.95 10.55 9.75 10.15
CN1 5.87 7.66 5.47 10.05 9.25 12.74 10.35 10.75 10.55 9.25 9.65
239
Appendix 2 Monthly water column BOD mg/L data for the Milltown Lake catchment provided by the National Source Protection
Pilot Project
Site May-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 May-07 Jun-07 Median
D2 3.08 2.85 1.40 - 1.19 3.78 1.50 - 2.18
TV1 7.88 3.48 - 1.60 0.90 1.29 2.78 1.29 1.60
TV2 3.78 2.29 2.99 2.89 1.49 2.59 1.89 3.18 2.74
TV3 1.99 1.99 1.09 1.90 1.49 1.29 2.98 1.89 1.90
TV4 3.38 0.80 2.29 1.56 2.49 1.59 2.09 0.01 1.84
TV5 3.28 3.65 1.89 1.49 1.79 2.78 3.88 1.00 2.34
TV6 3.08 1.95 1.49 1.19 1.99 1.99 1.59 1.29 1.77
TV7 5.37 3.95 2.69 1.59 1.39 2.48 0.75 0.99 2.04
GO1 2.59 0.99 2.98 1.59 0.90 1.10 2.89 3.58 2.09
GO2 4.16 - - 1.29 1.04 1.10 2.49 2.88 1.29
GO3 3.88 3.35 2.19 - 1.29 0.80 3.28 9.32 3.28
GO4 4.16 2.05 - 1.29 0.90 2.59 0.75 5.67 2.05
GO5 4.08 3.95 2.58 1.49 1.14 1.10 2.09 5.27 2.34
GO6 8.26 1.75 0.01 3.49 0.09 0.70 3.08 7.16 2.42
TH1 3.58 1.59 1.49 0.60 1.59 2.68 2.36 5.57 1.98
TH2 3.78 3.35 1.30 0.69 0.99 1.89 3.78 5.77 2.62
TH3 3.48 - - 1.09 1.59 2.39 3.98 5.37 2.94
TH4 6.57 3.65 1.20 0.00 0.79 1.79 3.48 8.06 2.64
TH5 4.16 2.12 2.79 1.49 0.69 1.30 3.88 7.36 2.46
TH6 10.35 2.12 2.09 0.90 1.69 1.00 2.69 6.97 2.11
MA2 4.16 3.38 - - 1.29 0.80 0.75 7.66 1.29
MA4 1.59 0.40 1.59 1.19 1.19 0.70 0.75 7.45 1.19
DG1 4.16 0.70 1.59 1.79 1.59 2.09 4.13 0.70 1.59
DG2 1.89 0.99 2.49 0.79 1.49 1.70 2.39 1.50 1.60
CN1 1.79 0.01 2.09 2.09 3.19 1.30 2.88 3.75 2.09
240
Appendix 2 Monthly water column TRP mg/L data for the Milltown Lake catchment provided by the National Source Protection Pilot
Project
Site Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Median
D2 0.079 0.105 0.092 0.049 0.050 0.047 0.092 0.053 0.107 0.069 0.074
TV1 0.350 0.019 0.079 0.087 0.112 0.062 0.054 0.037 0.058 0.033 0.060
TV2 0.155 - 0.074 0.056 0.087 0.039 0.041 0.031 0.147 0.094 0.074
TV3 0.374 0.100 0.089 0.053 0.069 0.044 0.041 0.036 0.145 0.314 0.079
TV4 0.125 0.177 0.065 0.088 0.055 0.040 0.039 0.027 0.142 0.081 0.073
TV5 0.084 0.242 0.379 0.054 0.051 0.042 0.039 0.030 0.075 0.087 0.065
TV6 0.115 0.085 0.272 0.043 0.047 0.048 0.039 0.031 0.079 0.102 0.064
TV7 0.126 0.092 0.426 0.069 0.057 0.052 0.044 0.030 0.109 0.095 0.080
GO1 0.087 0.161 0.100 0.057 0.033 0.029 0.039 0.017 0.215 0.090 0.072
GO2 0.043 0.050 0.284 0.039 0.037 0.027 0.035 0.017 0.074 0.070 0.041
GO3 - 0.044 0.447 0.050 0.030 0.019 0.029 0.014 0.041 0.038 0.038
GO4 0.055 0.155 0.190 0.054 0.027 0.017 0.025 0.011 0.038 0.029 0.034
GO5 0.057 0.079 0.424 0.059 0.039 0.034 0.042 0.020 0.063 0.047 0.052
GO6 0.062 0.077 0.080 0.062 0.043 0.030 0.040 0.019 0.054 0.048 0.051
TH1 0.107 0.244 0.077 0.068 0.044 0.033 0.025 0.020 0.074 0.061 0.064
TH2 0.029 0.247 0.554 0.047 0.039 0.026 0.018 0.013 0.368 0.055 0.043
TH3 0.020 0.069 0.381 0.071 0.040 0.024 0.025 0.019 0.038 0.053 0.039
TH4 0.080 0.088 0.639 0.055 0.675 0.064 0.046 0.045 0.075 0.051 0.069
TH5 0.052 0.079 0.372 0.051 0.055 0.047 0.044 0.140 0.036 0.056 0.054
TH6 0.062 0.094 0.505 0.056 0.074 0.049 0.042 0.032 0.027 0.054 0.055
MA2 0.194 0.446 0.058 0.048 0.107 0.068 0.118 0.428 0.158 0.144 0.131
MA4 0.065 0.271 0.015 0.055 0.022 0.011 0.026 0.147 0.058 0.027 0.041
DG1 0.360 0.074 0.009 0.043 0.019 0.007 0.017 - 0.100 0.065 0.043
DG2 0.435 0.104 0.140 0.055 0.060 0.046 0.044 0.047 0.150 0.102 0.081
CN1 0.509 0.057 0.053 0.048 0.017 - 0.011 - 0.065 0.037 0.051
241
Appendix 2 Monthly water column NH4 mg/L data for the Milltown Lake catchment provided by the National Source Protection Pilot
Project
Site Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Median
D2 0.022 0.021 0.020 0.030 0.059 0.202 1.316 0.697 0.216 0.127 0.093
TV1 0.030 0.069 0.061 0.116 0.310 0.605 1.162 0.122 0.004 0.030 0.092
TV2 0.035 0.026 0.042 0.058 0.118 0.138 0.298 0.052 0.182 0.019 0.055
TV3 0.340 0.019 0.021 0.031 0.075 0.194 0.267 0.090 0.384 0.787 0.142
TV4 0.051 0.224 0.040 0.040 0.081 0.431 0.357 0.118 0.839 0.475 0.171
TV5 0.016 0.032 0.046 0.045 0.060 0.385 0.281 0.150 0.119 0.095 0.077
TV6 0.037 0.032 0.047 0.041 0.052 0.407 0.234 0.108 0.069 0.101 0.061
TV7 0.018 0.036 0.074 0.040 0.075 0.409 0.265 0.124 0.137 0.037 0.074
GO1 0.034 0.128 0.046 0.037 0.018 0.085 0.386 0.050 1.750 0.370 0.067
GO2 - 0.006 0.032 0.035 0.058 0.054 0.175 0.045 1.026 0.161 0.054
GO3 - 0.007 0.027 0.015 0.009 0.022 0.055 0.008 0.108 0.077 0.022
GO4 - 0.006 0.021 0.018 0.006 0.008 0.020 - - 0.041 0.018
GO5 0.007 0.021 0.022 0.030 0.017 0.157 0.298 0.080 0.329 0.225 0.055
GO6 - 0.028 0.019 0.025 0.060 0.085 0.161 0.056 0.029 0.041 0.041
TH1 0.036 0.145 0.030 0.015 0.011 0.158 0.094 0.081 0.121 0.072 0.077
TH2 - 0.022 0.021 0.016 0.018 0.049 0.021 0.019 0.654 0.107 0.021
TH3 - 0.028 0.024 0.060 0.074 0.245 0.246 0.107 0.541 0.038 0.074
TH4 - 0.028 0.064 0.057 1.736 0.240 0.167 0.074 0.469 0.524 0.167
TH5 - 0.017 0.046 0.042 0.059 0.227 0.187 3.284 0.070 0.181 0.070
TH6 - 0.017 0.026 0.035 0.027 0.107 0.167 0.018 0.025 0.215 0.027
MA2 0.018 0.267 - 0.019 0.179 0.170 0.515 3.467 0.195 0.152 0.179
MA4 0.016 0.218 - - - 0.048 0.125 0.089 0.034 0.107 0.089
DG1 0.108 0.025 0.025 0.012 0.050 0.139 0.124 0.049 0.389 0.258 0.079
DG2 0.054 0.022 0.015 0.021 0.020 0.082 0.090 0.099 0.205 0.115 0.068
CN1 0.510 0.071 0.006 0.026 0.065 0.209 0.236 0.112 0.157 0.106 0.109
242
Appendix 2 Monthly water column Temperature ºC data for the Milltown Lake catchment provided by the National Source Protection
Pilot Project
Site May-06 Jun-06 Aug-06 Sep-06 Oct-06 Dec-06 Jan-07 Feb-07 Apr-07 May-07 Median
D2 10.80 - 16.80 15.30 11.80 7.10 5.80 5.70 14.90 16.10 11.30
TV1 10.20 18.60 15.00 16.30 12.80 6.10 4.50 5.00 13.90 14.20 13.35
TV2 11.20 16.10 14.40 0.00 12.80 6.80 4.90 5.70 10.30 12.40 10.75
TV3 8.70 17.00 12.80 15.20 12.60 6.90 5.40 6.10 12.40 12.80 12.50
TV4 8.90 17.40 12.90 15.60 12.70 7.00 5.20 5.70 13.40 13.10 12.80
TV5 10.20 - 15.00 14.20 11.30 7.20 5.10 5.80 11.90 12.70 10.75
TV6 10.20 - 15.70 14.20 11.50 7.20 5.10 5.50 14.00 13.80 10.85
TV7 10.30 - 16.10 14.20 12.10 7.00 5.20 5.50 12.80 13.70 11.20
GO1 10.60 16.00 15.50 14.80 12.70 8.00 6.00 5.60 14.30 16.70 13.50
GO2 10.70 - 15.90 15.40 11.90 8.20 6.10 5.90 15.20 13.90 11.30
GO3 9.10 - 15.70 15.00 12.50 8.00 5.90 6.30 18.20 14.20 10.80
GO4 9.30 - 14.90 15.10 12.80 8.30 6.00 6.60 17.60 14.30 11.05
GO5 9.80 - 14.60 14.30 12.60 8.20 6.10 6.80 16.70 13.40 11.20
GO6 10.10 - 16.70 14.30 12.40 7.40 5.90 6.40 14.70 13.70 11.25
TH1 9.50 16.30 14.90 14.80 12.00 7.60 6.10 6.50 12.60 15.80 12.30
TH2 9.50 - 16.50 15.10 11.00 8.20 6.40 6.70 17.40 16.30 10.25
TH3 9.60 - 16.90 15.10 12.40 8.10 6.50 6.50 17.80 15.20 11.00
TH4 9.70 - 15.70 15.10 11.70 8.30 6.60 6.70 16.30 14.90 10.70
TH5 9.70 - 14.90 15.20 10.70 8.50 6.50 6.30 14.20 15.20 10.20
TH6 9.90 - 16.30 14.30 10.20 7.30 5.80 6.40 16.20 14.60 10.05
MA2 9.00 16.40 14.20 14.70 11.10 8.70 6.80 6.80 10.40 12.90 10.75
MA4 9.80 12.60 14.10 14.60 12.00 8.50 6.70 6.60 9.50 13.20 10.90
DG1 9.00 18.40 13.10 16.40 12.50 6.30 5.10 5.60 13.70 13.50 12.80
DG2 9.10 17.90 13.20 16.30 12.50 7.20 5.30 6.20 14.20 13.60 12.85
CN1 8.20 17.50 12.60 15.70 11.40 7.90 5.50 6.00 12.40 12.90 11.90
243
Appendix 2 Spearman‘s correlation coefficients between aquatic species abundance and site physico-chemical characteristics for June
2007. The first value is the correlation coefficient, the second is the p value. Asterisked values highlight significant results.
Characteristics with which no species were correlated are not presented. Species names are coded as in Table 3.3 Correlations
Foan Spem Oecr Lemi Rhyn Caob Amri Elca Chpo Glma Tyla Rona Brri Amfl Hylu
Cond. -0.051 -0.003 0.188 0.019 0.380 -0.231 0.469 0.316 0.046 -0.046 0.316 -0.210 -0.001 -0.368 0.054
0.814 0.989 0.379 0.932 0.067 0.278 0.021* 0.132 0.831 0.833 0.132 0.326 0.997 0.077 0.803
pH 0.178 -0.242 0.209 -0.511 -0.091 -0.340 -0.030 -0.347 0.302 0.164 -0.347 -0.134 0.057 0.012 -0.048
0.406 0.254 0.327 0.011* 0.673 0.104 0.888 0.097 0.151 0.444 0.097 0.531 0.792 0.955 0.822
Alk. 0.125 -0.164 0.641 -0.208 0.161 -0.209 -0.010 -0.288 0.083 0.046 -0.288 -0.259 -0.183 -0.018 0.242
0.559 0.443 0.001* 0.330 0.452 0.328 0.964 0.172 0.700 0.832 0.172 0.221 0.392 0.933 0.254
DO % 0.014 -0.458 0.140 -0.174 -0.065 -0.546 -0.170 -0.346 -0.205 0.155 -0.346 -0.129 0.051 0.177 0.179
0.949 0.024* 0.515 0.415 0.763 0.006* 0.428 0.097 0.337 0.470 0.097 0.547 0.814 0.408 0.402
NH4 -0.040 0.029 0.220 -0.076 0.440 -0.319 -0.123 0.167 -0.099 -0.339 0.167 -0.105 -0.183 -0.419 -0.080
0.853 0.893 0.301 0.723 0.032* 0.128 0.567 0.436 0.644 0.105 0.436 0.626 0.393 0.042* 0.709
TRP -0.403 0.007 0.276 -0.025 0.240 -0.322 0.328 0.046 -0.332 -0.083 0.046 0.119 -0.648 -0.416 0.049
0.051 0.974 0.192 0.908 0.259 0.125 0.118 0.831 0.114 0.698 0.831 0.580 0.001* 0.043* 0.819
Width -0.317 -0.005 0.541 0.261 0.025 -0.259 0.231 0.076 -0.026 -0.330 0.076 -0.366 -0.392 0.073 -0.218
0.132 0.980 0.006* 0.218 0.908 0.222 0.278 0.724 0.905 0.115 0.724 0.078 0.059 0.735 0.305
Substrate 0.103 0.013 0.324 0.143 0.317 -0.020 0.286 0.332 0.326 -0.021 0.332 -0.464 -0.015 -0.181 -0.228
0.633 0.951 0.123 0.504 0.132 0.925 0.176 0.113 0.120 0.922 0.113 0.022* 0.944 0.399 0.284
Bridge 0.033 0.292 0.442 0.148 0.094 0.295 0.023 0.162 0.578 -0.228 0.162 -0.239 0.033 -0.123 -0.134
0.879 0.166 0.031* 0.490 0.664 0.162 0.916 0.451 0.003* 0.285 0.451 0.261 0.878 0.567 0.532
Order 0.143 -0.240 0.539 -0.177 0.262 -0.188 0.134 -0.133 0.380 -0.241 -0.133 -0.259 -0.094 -0.190 -0.060
0.504 0.258 0.007* 0.408 0.217 0.380 0.532 0.536 0.067 0.257 0.536 0.221 0.662 0.375 0.779
Slope 0.172 -0.416 -0.273 -0.554 0.040 -0.367 -0.421 -0.258 0.083 -0.046 -0.258 0.005 0.022 -0.298 -0.221
0.421 0.043* 0.196 0.005* 0.854 0.078 0.041* 0.224 0.700 0.832 0.224 0.982 0.920 0.157 0.299
Slope -0.012 -0.375 0.413 -0.279 -0.090 -0.505 -0.135 -0.094 -0.007 -0.171 -0.094 -0.270 -0.049 -0.026 -0.202
Cat. 0.955 0.071 0.045* 0.187 0.677 0.012* 0.530 0.661 0.975 0.424 0.661 0.202 0.820 0.905 0.344
N 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
244
Appendix 3 Sediment physico-chemical characteristics for the Milltown Lake catchment, June 2006 and September 2006
Site Season V
Status %Sand %
Silt/Clay TP
mg/g Fe:P %
TN % OM %C C:N Mn
mg/g Mg
mg/g Ca
mg/g Fe
mg/g K
mg/g Na
mg/g Zn
µg/g Cu
µg/g
D1 J06 NV 98.44 1.56 0.24 2.53 0.17 20.13 11.68 70.34 0.06 0.21 0.19 0.61 0.05 0.61 16.83 25.50
D2 J06 NV 100.00 0.00 0.28 3.03 0.28 22.79 13.22 47.18 0.12 0.23 0.22 0.84 0.07 0.82 19.95 24.50
TV1 J06 NV 100.00 0.00 0.23 2.19 0.31 18.69 10.84 35.18 0.04 0.23 0.20 0.50 0.05 0.72 4.91 16.50
TV2 J06 NV 99.26 0.74 0.36 1.11 0.18 20.67 11.99 65.84 0.03 0.18 0.16 0.40 0.09 1.00 6.60 19.88
TV3 J06 NV 98.60 1.40 0.22 20.07 11.64 53.62 0.10 0.25 0.15 0.67 0.13 0.81 18.39 19.82
TV4 J06 NV 98.37 1.63 0.20 4.23 0.09 20.14 11.68 133.43 0.13 0.30 0.13 0.86 0.16 0.58 13.01 20.63
TV5 J06 NV 100.00 0.00 0.26 1.04 0.09 23.06 13.37 152.78 0.06 0.28 0.16 0.27 0.04 0.66 15.13 19.00
TV6 J06 NV 97.80 2.20 0.11 10.98 6.37 60.62 0.14 0.29 0.15 0.86 0.25 0.85 11.16 20.50
TV7 J06 NV 100.00 0.00 0.26 2.41 0.29 20.19 11.71 40.78 0.09 0.22 0.13 0.63 0.19 0.79 3.65 29.25
GO1 J06 NV 96.97 3.03 0.10 4.15 0.05 15.56 9.02 184.09 0.08 0.19 0.06 0.43 0.15 0.70 20.50 21.00
GO2 J06 NV 100.00 0.00 0.29 2.98 0.38 25.98 15.07 39.84 0.13 0.27 0.15 0.85 0.14 0.59 39.36 12.38
GO3 J06 NV 100.00 0.00 0.34 2.36 0.31 28.19 16.35 53.42 0.17 0.22 0.14 0.80 0.16 0.84 43.15 19.50
GO4 J06 NV 98.57 1.43 0.12 3.78 0.22 67.65 39.24 175.08 0.08 0.17 0.05 0.44 0.18 0.92 14.90 19.50
GO5 J06 NV 96.10 3.90 0.20 2.90 0.20 20.87 12.10 59.60 0.04 0.26 0.18 0.58 0.11 0.70 24.58 22.13
GO6 J06 NV 100.00 0.00 0.29 3.32 0.36 22.80 13.22 36.31 0.13 0.28 0.17 0.95 0.04 0.63 49.59 4.38
TH1 J06 NV 100.00 0.00 0.27 2.17 0.13 16.90 9.80 73.66 0.05 0.27 0.23 0.59 0.08 0.75 33.69 20.00
TH2 J06 NV 97.62 2.38 0.26 2.56 0.26 17.65 10.24 39.51 0.06 0.22 0.17 0.67 0.06 1.00 17.44 23.38
TH3 J06 NV 100.00 0.00 0.24 2.87 0.28 19.38 11.24 40.63 0.12 0.26 0.19 0.70 0.22 1.13 20.89 26.75
TH4 J06 NV 93.75 6.25 0.25 3.61 0.20 16.14 9.36 47.74 0.14 0.32 0.05 0.89 0.21 0.77 9.74 29.13
MA2 J06 NV 97.56 2.44 0.11 4.12 0.09 11.25 6.53 74.53 0.01 0.24 0.12 0.47 0.06 1.07 11.46 11.13
MA4 J06 NV 100.00 0.00 0.40 1.64 0.53 23.85 13.83 25.99 0.22 0.26 0.05 0.66 0.21 0.76 44.15 25.00
DG1 J06 NV 96.77 3.23 0.28 3.26 0.36 29.03 16.84 47.14 0.14 0.29 0.25 0.91 0.06 1.25 25.75 6.75
DG2 J06 NV 99.20 0.80 0.13 4.21 0.10 17.47 10.13 103.34 0.06 0.25 0.07 0.55 0.17 0.74 6.79 14.13
CN1 J06 NV 100.00 0.00 0.38 2.19 0.03 12.52 7.26 230.41 0.13 0.23 0.23 0.84 0.21 0.64 20.50 24.88
D1 J06 V 98.18 1.82 0.27 4.06 0.17 15.92 9.23 54.71 0.17 0.29 0.03 1.11 0.03 0.82 24.45 1.75
D2 J06 V 100.00 0.00 0.26 3.76 0.06 14.97 8.68 137.75 0.15 0.30 0.14 0.96 0.15 0.48 26.23 9.88
TV1 J06 V 100.00 0.00 0.24 1.87 0.36 30.10 17.46 48.40 0.06 0.21 0.22 0.45 0.20 1.30 2.10 12.00
TV3 J06 V 97.92 2.08 0.26 2.77 0.17 19.13 11.09 65.72 0.09 0.24 0.13 0.73 0.13 0.86 13.89 14.32
TV5 J06 V 98.86 1.14 0.26 2.49 0.23 22.15 12.85 55.59 0.10 0.25 0.13 0.63 0.16 0.57 3.70 3.50
TV7 J06 V 98.10 1.90 0.13 3.09 0.11 12.48 7.24 64.60 0.05 0.20 0.16 0.41 0.04 0.59 2.85 22.00
245
Appendix 3 Sediment physico-chemical characteristics for the Milltown Lake catchment, June 2006 and September 2006 Contd.
Site Season V
Status %Sand %
Silt/Clay TP
mg/g Fe:P % TN % OM %C C:N Mn
mg/L Mg
mg/L Ca
mg/L Fe
mg/L K
mg/L Na
mg/L Zn
µg/g Cu
µg/g
GO1 J06 V 97.78 2.22 0.10 4.92 0.14 19.56 11.34 80.99 0.03 0.23 0.18 0.51 0.05 0.71 2.80 17.50
GO6 J06 V 96.55 3.45 0.18 4.61 0.13 15.01 8.71 67.19 0.04 0.24 0.09 0.82 0.04 0.37 22.54 17.38
TH1 J06 V 98.36 1.64 0.22 3.17 0.09 15.27 8.86 93.67 0.05 0.26 0.07 0.70 0.14 0.51 21.38 8.75
TH2 J06 V 100.00 0.00 0.24 3.79 0.13 19.99 11.59 91.97 0.13 0.25 0.16 0.89 0.23 1.17 17.10 30.63
TH5 J06 V 100.00 0.00 0.34 2.46 0.22 22.49 13.04 60.08 0.13 0.23 0.09 0.83 0.23 1.11 25.91 20.13
CN1 J06 V 100.00 0.00 0.44 1.54 0.21 22.45 13.02 60.96 0.03 0.20 0.18 0.68 0.16 1.87 3.74 14.00
D1 S06 NV 100.00 0.00 0.16 3.71 0.28 15.72 9.12 32.45 0.07 0.17 0.17 0.60 0.02 0.58 13.36 25.25
D2 S06 NV 100.00 0.00 0.57 3.16 1.11 24.07 13.96 12.54 0.21 0.28 0.23 1.79 0.08 1.03 51.00 12.63
TV1 S06 NV 100.00 0.00 0.22 2.79 0.28 49.03 28.44 101.22 0.07 0.21 0.20 0.60 0.04 0.55 11.51 21.75
TV2 S06 NV 99.10 0.90 0.23 3.17 0.15 18.18 10.54 68.44 0.08 0.24 0.21 0.72 0.05 0.68 11.93 24.88
TV3 S06 NV 99.22 0.78 0.25 3.05 0.28 19.29 11.19 39.82 0.07 0.24 0.09 0.75 0.09 1.12 7.96 14.13
TV4 S06 NV 100.00 0.00 0.26 3.18 0.06 16.65 9.66 153.21 0.09 0.32 0.17 0.83 0.06 0.59 8.95 14.75
TV5 S06 NV 99.25 0.75 0.17 3.54 0.07 10.02 5.81 82.98 0.03 0.29 0.13 0.61 0.08 1.46 9.15 12.25
TV7 S06 NV 98.61 1.39 0.26 2.92 0.28 17.65 10.24 36.44 0.08 0.24 0.16 0.76 0.08 0.90 16.27 20.44
GO1 S06 NV 97.41 2.59 0.11 2.37 0.13 32.70 18.97 150.45 0.00 0.12 0.14 0.25 0.01 0.57 16.27 21.88
GO2 S06 NV 100.00 0.00 0.11 24.51 14.22 126.86 0.09 0.23 0.18 0.83 0.05 1.08 28.53 25.63
GO6 S06 NV 98.98 1.02 0.20 2.81 0.18 16.99 9.85 54.12 0.05 0.20 0.13 0.56 0.06 1.30 23.35 27.00
TH1 S06 NV 96.67 3.33 0.24 2.70 0.11 15.60 9.05 80.75 0.11 0.27 0.25 0.65 0.25 0.75 23.11 26.50
TH2 S06 NV 87.50 12.50 0.39 2.38 0.44 28.82 16.72 38.19 0.07 0.23 0.20 0.93 0.14 1.56 29.13 11.75
TH3 S06 NV 100.00 0.00 0.97 15.84 9.19 9.44 0.13 0.25 0.21 0.98 0.21 0.86 5.28 31.63
TH4 S06 NV 100.00 0.00 0.17 3.63 0.10 15.92 9.23 94.17 0.05 0.22 0.12 0.63 0.06 0.77 19.70 17.88
TH5 S06 NV 99.15 0.85 0.23 3.10 0.13 13.64 7.91 62.76 0.06 0.29 0.09 0.71 0.06 0.54 10.98 23.88
DG1 S06 NV 96.39 3.61 0.13 5.27 0.08 17.68 10.25 122.02 0.02 0.26 0.08 0.67 0.07 0.96 7.55 15.25
D2 S06 V 98.15 1.85 0.29 3.85 0.09 12.43 7.21 79.18 0.13 0.25 0.19 1.11 0.01 0.59 23.01 4.75
TV5 S06 V 100.00 0.00 0.15 3.81 0.16 15.94 9.25 57.39 0.04 0.18 0.13 0.57 0.04 0.83 9.21 22.63
TV7 S06 V 100.00 0.00 0.16 4.31 0.14 17.90 10.38 74.12 0.07 0.22 0.18 0.70 0.05 0.76 15.86 14.90
GO1 S06 V 98.56 1.44 0.15 2.94 0.17 11.01 6.39 37.99 0.01 0.24 0.13 0.45 0.09 1.33 15.86 14.13
GO6 S06 V 98.63 1.37 0.14 4.13 0.18 17.27 10.02 55.01 0.10 0.18 0.22 0.58 0.05 0.54 16.49 11.75
TH1 S06 V 97.18 2.82 0.21 3.27 0.16 12.66 7.34 45.58 0.07 0.22 0.18 0.70 0.05 0.76 15.86 14.90
TH2 S06 V 97.22 2.78 0.31 2.46 0.28 25.20 14.62 51.53 0.06 0.26 0.21 0.76 0.04 0.49 30.66 21.25
246
Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, May 2006
Order Taxon D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 GO1 GO2 GO3 GO4
Amphipoda Gammarus duebeni celticus Stock and Pinkster 3 6 649 0 1 3 0 6 1 15 10 49 Gammarus pulex L. 5 3 0 239 111 695 148 0 9 1 6 12 Bivalvia Pisidium spp. 0 0 0 0 0 0 0 0 0 0 0 0 Sphaerium spp. 0 0 3 0 0 1 0 0 0 0 0 0 Coleoptera Curculionidae 0 0 0 0 0 0 0 0 0 0 0 0 Dytiscidae 0 0 0 0 0 0 0 0 0 0 0 0 Elminthidae 103 87 0 33 23 11 127 28 1 68 122 26 Gyrinidae 0 0 0 0 0 0 0 0 0 0 0 0 Helodidiae 0 0 0 0 0 0 0 0 0 0 0 1 Diptera Chironomidae 67 0 6 11 3 1 14 33 70 47 106 41 Chironomus spp. 0 0 0 0 0 0 0 0 0 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 0 0 0 Simuliidae 0 0 0 0 0 0 0 0 7 1 0 1 Tipulidae 0 0 0 8 0 0 0 0 7 0 0 0 Ephemeroptera Baetidae 1 0 0 2 0 0 0 17 0 34 61 15 Baetis rhodani Pictet 72 17 0 196 366 640 154 18 466 250 191 23 Caenidae 27 6 0 0 0 0 0 7 0 0 0 0 Ecdyonourus spp. 1 0 0 0 0 0 0 4 2 22 0 1 Leptophlebiidae 0 0 0 0 0 0 0 0 0 0 0 0 Rhithrogena semicolorata Curtis 29 0 0 4 8 90 0 86 11 218 73 196 Seratella ignita Poda 0 1 0 0 0 0 0 0 0 0 0 0 Gastropoda Ancylus fluviatilis Muller 3 26 1 17 6 1 3 5 1 69 16 8 Planorbidae 0 0 0 0 0 0 0 0 0 0 0 0 Radix balthica L. 0 0 0 0 0 0 0 0 0 0 0 0 Hirudinea Hirudinea 0 0 10 0 0 1 0 1 2 1 0 0 Isopoda Asellidae 4 0 306 15 11 5 0 0 1 0 0 0 Megaloptera Sialidae 0 0 0 0 0 0 0 0 0 0 0 0 Oligochaeta Enchytraeidae 0 0 0 0 0 0 0 0 0 0 0 0 Lumbricidae 0 0 0 0 0 0 0 0 1 0 0 0 Lumbriculidae 0 0 0 0 0 0 0 0 0 0 0 0 Naidae 0 0 0 0 0 0 0 0 0 0 0 0 Tubificidae 0 0 0 0 0 0 0 0 1 0 0 0 Plecoptera Chloroperlidae 0 0 0 18 0 0 0 39 0 1 7 3 Leuctridae 0 0 0 0 0 0 0 1 1 22 0 12 Nemouridae 0 0 0 0 0 2 0 1 5 18 25 0 Perlidae 0 0 0 0 0 0 0 0 0 0 0 1 Perlodidae 0 0 0 2 3 0 0 6 3 4 0 6 Taeiniopterygidae 0 0 0 0 0 4 0 0 20 4 0 2 Trichoptera Glossosomatidae 1 0 0 0 0 0 2 7 0 59 14 154 Hydropsyche siltalai Döhler 1 1 0 0 0 0 0 5 0 5 47 6 Hydroptillidae 0 0 0 0 0 0 0 2 0 0 0 0 Lepidostomatidae 0 0 0 0 0 0 0 0 0 0 0 0 Leptoceridae 0 0 0 0 0 0 0 0 0 6 0 0 Limnephillidae 2 5 2 11 2 10 7 8 15 9 9 22 Odontocerum albicorne Spence 0 0 0 0 0 0 0 0 0 0 0 1 Philopotamidae 0 0 0 0 0 0 0 0 0 0 0 0 Polycentropodidae 0 0 1 1 0 2 0 1 12 9 0 2 Psychoididae 0 0 0 0 0 0 0 0 0 0 0 0 Ryacophillidae 0 0 0 0 0 2 0 1 2 4 0 1 Sericostoma personatum Spence 0 0 0 0 0 0 0 0 0 4 0 1
247
Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, May 2006 Contd.
Order Taxon GO5 GO6 TH1 TH2 TH3 TH4 TH6 MA2 MA4 DG1 DG2 CN1
Amphipoda Gammarus duebeni celticus Stock and Pinkster 28 0 169 57 16 12 24 55 47 11 6 31 Gammarus pulex L. 82 0 51 2 12 0 41 0 0 163 331 60 Bivalvia Pisidium spp. 1 0 0 0 0 1 0 0 0 0 0 0 Sphaerium spp. 0 0 0 0 0 0 0 2 1 0 0 1 Coleoptera Curculionidae 0 0 0 0 0 0 0 0 0 0 0 0 Dytiscidae 0 0 0 0 0 0 0 1 0 0 0 0 Elminthidae 67 755 4 92 92 97 89 0 14 17 28 11 Gyrinidae 0 0 0 0 0 0 0 0 0 0 0 0 Helodidiae 1 0 3 0 0 0 0 0 0 1 0 0 Diptera Chironomidae 37 170 173 218 96 53 13 16 106 3 6 109 Chironomus spp. 0 0 0 0 0 0 0 0 0 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 0 0 0 Simuliidae 0 2 8 0 0 0 0 0 4 4 1 0 Tipulidae 0 0 3 0 0 0 0 18 6 18 32 0 Ephemeroptera Baetidae 12 3 33 148 78 7 0 4 9 2 0 27 Baetis rhodani Pictet 126 89 128 497 232 37 63 35 45 41 56 173 Caenidae 1 467 0 1 5 0 2 0 0 0 0 0 Ecdyonourus spp. 1 1 0 100 30 2 3 0 2 0 0 1 Leptophlebiidae 0 0 0 0 0 0 0 0 0 0 0 0 Rhithrogena semicolorata Curtis 125 95 1 269 197 118 61 159 115 215 307 3 Seratella ignita Poda 0 1 0 0 1 0 0 0 0 0 0 0 Gastropoda Ancylus fluviatilis Muller 0 10 0 17 12 28 5 14 11 32 3 0 Planorbidae 0 0 0 0 0 0 0 0 0 0 0 0 Radix balthica L. 0 0 0 0 0 0 0 0 0 0 0 1 Hirudinea Hirudinea 0 0 3 0 0 0 0 0 0 0 0 6 Isopoda Asellidae 2 11 87 0 0 0 0 0 1 0 1 86 Megaloptera Sialidae 0 0 0 0 0 0 0 0 0 0 0 0 Oligochaeta Enchytraeidae 0 0 0 0 0 0 0 0 0 0 0 0 Lumbricidae 0 0 0 0 0 0 0 0 0 0 0 0 Lumbriculidae 0 0 5 0 0 0 0 0 0 0 0 0 Naidae 0 0 0 0 0 0 0 0 0 0 0 0 Tubificidae 0 0 1 0 0 0 0 0 0 0 0 0 Plecoptera Chloroperlidae 0 1 0 65 46 1 0 6 2 0 0 0 Leuctridae 0 3 0 38 17 20 0 21 23 0 3 0 Nemouridae 3 2 17 117 52 30 0 2 1 0 3 3 Perlidae 0 0 0 0 0 0 0 0 0 0 0 0 Perlodidae 4 1 0 26 50 2 2 5 6 6 7 23 Taeiniopterygidae 2 0 6 1 4 2 0 9 1 2 1 1 Trichoptera Glossosomatidae 0 1 45 17 7 10 49 2 26 30 32 1 Hydropsyche siltalai Döhler 6 130 0 67 32 46 7 1 1 2 0 11 Hydroptillidae 0 0 0 0 0 0 0 0 0 0 0 0 Lepidostomatidae 0 0 0 0 0 0 0 0 0 0 0 0 Leptoceridae 1 1 1 1 2 0 1 0 6 0 4 1 Limnephillidae 10 52 1 12 15 7 14 2 19 10 20 33 Odontocerum albicorne Spence 0 3 0 0 0 0 1 0 0 0 0 0 Philopotamidae 0 0 0 0 0 0 0 4 23 0 0 0 Polycentropodidae 2 1 3 26 7 1 0 15 3 0 0 3 Psychoididae 0 0 0 0 0 0 0 0 0 0 0 0 Ryacophillidae 0 0 0 0 2 0 0 0 3 0 0 0 Sericostoma personatum Spence 2 0 0 1 2 0 9 0 0 1 5 3
248
Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, October 2006
Order Taxon D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3 GO4
Amphipoda Gammarus duebeni celticus Stock and Pinkster 23 36 1148 0 17 5 29 277 1 46 45 275 131 Gammarus pulex L. 243 213 0 1023 324 2408 754 2381 1102 0 0 0 8 Bivalvia Pisidium spp. 0 0 0 0 0 0 2 0 0 0 0 0 0 Sphaerium spp. 0 0 71 0 5 0 0 0 0 0 1 0 0 Coleoptera Curculionidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Dytiscidae 0 0 0 0 0 0 0 0 1 0 0 0 0 Elminthidae 314 370 0 16 174 20 1369 98 920 34 61 573 56 Gyrinidae 0 3 0 0 0 0 0 0 0 0 0 3 0 Helodidiae 0 0 0 0 0 0 0 0 0 0 0 0 5 Diptera Chironomidae 17 36 352 1 20 13 209 2 34 7 16 31 7 Chironomus spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Simuliidae 3 37 4 9 19 1 0 0 0 145 24 3 22 Tipulidae 0 0 0 4 6 1 0 0 0 30 0 0 0 Ephemeroptera Baetidae 0 0 0 0 0 0 0 0 0 5 1 2 0 Baetis rhodani Pictet 142 144 0 10 45 121 8 8 12 369 126 57 3 Caenidae 6 8 0 0 0 0 0 0 0 0 0 0 1 Ecdyonourus spp. 24 6 0 0 0 0 0 0 0 0 23 72 17 Leptophlebiidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Rhithrogena semicolorata Curtis 22 19 0 2 0 2 0 0 0 0 39 52 126 Seratella ignita Poda 0 0 0 0 0 0 0 0 0 0 0 0 0 Gastropoda Ancylus fluviatilis Muller 29 7 1 20 60 0 2 0 0 1 24 15 6 Planorbidae 0 0 0 0 0 0 0 0 0 0 1 1 0 Radix balthica L. 1 0 0 0 1 0 0 0 0 0 1 0 0 Hirudinea Hirudinea 0 4 83 6 8 5 2 0 5 11 1 2 1 Isopoda Asellidae 11 21 154 14 34 0 5 1 17 42 1 0 0 Megaloptera Sialidae 0 0 1 0 0 0 0 0 0 5 0 0 0 Oligochaeta Enchytraeidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbricidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbriculidae 0 0 0 0 0 0 1 0 0 0 0 0 0 Naidae 1 0 0 0 0 0 0 0 0 0 0 0 0 Tubificidae 2 0 0 0 1 0 0 0 0 0 0 0 0 Plecoptera Chloroperlidae 0 0 0 0 0 0 0 0 0 0 0 7 1 Leuctridae 0 0 0 0 0 0 0 0 0 0 1 5 1 Nemouridae 1 0 0 0 0 0 1 0 0 0 1 15 0 Perlidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Perlodidae 0 0 0 0 0 0 0 0 0 1 0 6 0 Taeiniopterygidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Trichoptera Glossosomatidae 1 0 0 125 5 1 176 3 0 2 110 50 0 Hydropsyche siltalai Döhler 89 60 0 0 0 0 3 0 2 0 19 104 19 Hydroptillidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lepidostomatidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Leptoceridae 1 1 0 0 0 0 0 0 0 0 0 0 0 Limnephillidae 44 35 18 15 27 17 17 86 51 15 12 0 52 Odontocerum albicorne Spence 0 0 0 0 0 0 0 0 0 0 0 19 2 Philopotamidae 0 0 0 0 0 0 0 0 0 0 0 6 16 Polycentropodidae 1 0 3 0 0 0 0 0 0 21 3 45 11 Psychoididae 0 0 0 0 0 0 0 1 0 0 0 0 0 Ryacophillidae 0 1 0 0 3 0 0 0 0 0 1 9 4 Sericostoma personatum Spence 1 1 0 0 0 1 1 1 0 0 8 2 12
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Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, October 2006 Contd.
Order Taxon GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2 CN1
Amphipoda Gammarus duebeni celticus Stock and Pinkster 81 161 392 29 12 65 1 3 233 36 10 4 545 Gammarus pulex L. 618 163 0 0 0 0 0 0 0 0 439 925 271 Bivalvia Pisidium spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Sphaerium spp. 0 0 41 0 0 0 0 0 0 0 1 4 2 Coleoptera Curculionidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Dytiscidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Elminthidae 37 482 32 317 120 331 838 123 12 141 54 73 70 Gyrinidae 0 0 0 0 0 23 0 0 0 0 0 0 0 Helodidiae 2 1 0 0 0 0 0 0 8 2 7 0 0 Diptera Chironomidae 151 7 112 165 30 67 79 7 3 48 1 0 25 Chironomus spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Simuliidae 23 18 14 59 28 25 33 16 45 48 5 8 47 Tipulidae 0 0 0 0 0 0 0 0 0 14 22 47 4 Ephemeroptera Baetidae 1 0 0 0 0 2 1 0 1 2 0 0 0 Baetis rhodani Pictet 18 17 22 564 43 397 689 130 3 123 60 149 59 Caenidae 0 1 0 0 1 0 0 1 0 0 0 0 0 Ecdyonourus spp. 21 6 0 27 56 26 13 10 2 5 1 5 0 Leptophlebiidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Rhithrogena semicolorata Curtis 21 13 0 48 103 68 45 10 35 38 97 47 0 Seratella ignita Poda 0 0 0 0 0 0 0 0 0 0 0 0 0 Gastropoda Ancylus fluviatilis Muller 2 37 0 6 3 7 2 6 0 9 131 12 21 Planorbidae 1 0 0 0 0 0 0 0 0 0 0 0 0 Radix balthica L. 0 1 1 0 0 0 0 0 0 0 0 0 0 Hirudinea Hirudinea 2 4 1239 1 2 1 2 1 0 1 1 0 20 Isopoda Asellidae 13 5 0 4 0 0 63 173 0 1 0 0 278 Megaloptera Sialidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Oligochaeta Enchytraeidae 0 0 0 0 0 0 1 0 0 0 0 0 0 Lumbricidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbriculidae 0 0 0 0 0 0 1 0 0 0 0 0 0 Naidae 0 1 0 0 0 0 3 0 0 0 0 0 0 Tubificidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Plecoptera Chloroperlidae 0 0 0 1 2 1 0 0 0 3 0 0 0 Leuctridae 0 0 0 3 9 9 0 0 0 1 0 0 0 Nemouridae 0 0 0 6 7 14 1 0 0 2 0 0 1 Perlidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Perlodidae 0 0 0 0 1 1 0 0 8 30 0 0 1 Taeiniopterygidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Trichoptera Glossosomatidae 8 389 5 83 44 147 10 0 143 64 583 542 3 Hydropsyche siltalai Döhler 0 31 0 40 129 219 134 198 0 7 0 0 0 Hydroptillidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lepidostomatidae 0 0 0 0 0 0 0 0 0 18 0 0 0 Leptoceridae 0 0 1 0 0 0 0 0 0 16 1 0 0 Limnephillidae 10 20 24 14 27 78 19 21 59 28 35 28 18 Odontocerum albicorne Spence 1 2 1 0 0 0 0 0 0 0 0 0 0 Philopotamidae 0 0 0 0 0 0 0 0 12 59 0 0 0 Polycentropodidae 23 0 20 27 4 1 0 0 7 1 0 1 22 Psychoididae 0 0 0 0 0 0 0 0 0 0 0 0 0 Ryacophillidae 0 3 0 1 5 8 0 0 0 1 1 0 0 Sericostoma personatum Spence 1 20 1 1 1 18 0 0 0 5 6 3 31
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Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, May 2007
Order Taxon D1 D2 TV1 TV2 TV3 TV4 TV5 TV6 TV7 GO1 GO2 GO3 GO4
Amphipoda Gammarus duebeni celticus Stock and Pinkster 1 7 1856 1 2 0 2 0 2 6 17 5 67 Gammarus pulex L. 977 136 12 1426 642 1776 776 2238 752 77 6 5 6 Bivalvia Pisidium spp. 0 0 0 0 0 0 14 0 6 0 0 0 0 Sphaerium spp. 0 0 10 0 4 0 0 0 0 0 0 0 0 Coleoptera Curculionidae 0 0 0 0 1 1 0 0 0 1 0 0 0 Dytiscidae 0 0 0 0 0 0 0 0 0 3 1 0 0 Elminthidae 827 522 0 22 50 109 522 82 1059 3 93 208 52 Gyrinidae 0 1 0 0 0 0 0 0 0 0 0 2 0 Helodidiae 0 0 0 0 0 0 0 0 0 0 0 0 0 Diptera Chironomidae 111 146 155 11 66 3 119 18 178 313 58 201 46 Chironomus spp. 0 0 0 0 0 0 0 0 0 32 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 10 0 0 0 Simuliidae 1 4 0 4 0 0 0 0 0 8 0 3 0 Tipulidae 0 0 0 0 2 0 0 0 0 3 0 0 0 Ephemeroptera Baetidae 1 20 0 0 2 0 0 0 0 56 83 52 7 Baetis rhodani Pictet 333 567 0 104 180 116 153 197 540 76 208 72 133 Caenidae 42 126 0 0 0 0 0 0 0 0 2 2 0 Ecdyonourus spp. 5 29 0 0 0 0 1 0 0 0 11 5 13 Leptophlebiidae 0 0 0 0 0 0 0 0 0 10 0 0 0 Rhithrogena semicolorata Curtis 271 309 0 0 13 2 23 1 11 2 302 124 182 Seratella ignita Poda 0 0 0 0 0 0 0 0 0 1 0 0 0 Gastropoda Ancylus fluviatilis Muller 10 1 5 32 36 6 2 1 4 0 2 8 0 Planorbidae 0 0 0 0 0 0 1 0 0 0 0 0 0 Radix balthica L. 0 0 1 0 0 0 0 0 0 0 0 0 0 Hirudinea Hirudinea 0 0 6 0 1 1 0 1 0 12 0 0 0 Isopoda Asellidae 1 1 285 2 1 0 3 1 7 0 0 0 0 Megaloptera Sialidae 0 0 0 0 0 0 0 0 0 1 0 0 0 Oligochaeta Enchytraeidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbricidae 0 0 0 0 1 0 0 0 0 16 0 0 0 Lumbriculidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Naidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Tubificidae 1 0 0 0 2 0 3 0 0 4 0 0 0 Plecoptera Chloroperlidae 1 0 0 4 0 0 0 0 0 0 7 17 9 Leuctridae 6 2 0 0 0 0 0 0 0 0 10 5 2 Nemouridae 0 0 0 0 0 0 0 0 0 2 0 23 1 Perlidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Perlodidae 0 0 0 0 17 0 3 0 0 1 30 0 4 Taeiniopterygidae 0 0 0 2 0 0 0 0 2 0 1 0 3 Trichoptera Glossosomatidae 0 0 0 4 5 1 57 3 2 0 19 4 101 Hydropsyche siltalai Döhler 283 228 0 0 0 0 2 0 15 0 0 6 13 Hydroptillidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lepidostomatidae 14 7 0 0 0 0 11 0 0 0 0 0 1 Leptoceridae 16 4 0 0 0 0 0 0 0 1 0 4 0 Limnephillidae 14 10 1 2 19 5 40 3 48 1 7 3 5 Odontocerum albicorne Spence 0 0 0 0 0 0 0 0 0 0 0 0 0 Philopotamidae 0 0 0 0 0 0 0 1 0 0 1 1 5 Polycentropodidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Psychoididae 0 0 5 3 0 0 0 0 1 1 8 3 2 Ryacophillidae 1 0 0 0 1 1 0 0 3 0 4 2 0 Sericostoma personatum Spence 1 1 0 0 0 0 3 0 0 0 0 0 1
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Appendix 4 Macroinvertebrate count data for the Milltown Lake catchment, May 2007 Contd.
Order Taxon GO5 GO6 TH1 TH2 TH3 TH4 TH5 TH6 MA2 MA4 DG1 DG2 CN1
Amphipoda Gammarus duebeni celticus Stock and Pinkster 11 28 148 1 2 3 0 0 81 262 5 1 179 Gammarus pulex L. 79 64 0 1 3 17 0 4 0 0 825 857 648 Bivalvia Pisidium spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Sphaerium spp. 0 0 0 0 0 0 0 0 0 0 3 0 13 Coleoptera Curculionidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Dytiscidae 0 0 1 0 0 0 0 0 0 0 0 0 0 Elminthidae 61 78 6 150 72 189 920 321 2 137 69 83 28 Gyrinidae 0 1 0 0 0 2 0 0 0 0 0 0 0 Helodidiae 0 0 31 0 0 0 0 0 0 0 4 0 1 Diptera Chironomidae 20 9 129 47 90 119 1886 1003 87 145 28 4 97 Chironomus spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Eristalsis spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 Simuliidae 0 1 0 0 0 2 2589 218 0 2 0 1 0 Tipulidae 0 0 4 0 0 0 0 0 2 10 19 4 2 Ephemeroptera Baetidae 13 3 47 36 58 18 0 4 60 59 5 0 8 Baetis rhodani Pictet 81 37 126 114 90 182 503 82 95 39 155 24 18 Caenidae 0 6 0 0 3 7 1042 1469 0 0 0 0 1 Ecdyonourus spp. 34 3 0 6 13 4 16 21 0 3 3 6 0 Leptophlebiidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Rhithrogena semicolorata Curtis 173 197 31 42 108 319 4 47 46 477 184 402 14 Seratella ignita Poda 0 0 0 0 0 0 0 2 0 0 0 0 0 Gastropoda Ancylus fluviatilis Muller 0 0 0 0 4 0 0 0 8 20 61 2 4 Planorbidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Radix balthica L. 0 0 0 0 0 0 0 0 0 0 0 0 0 Hirudinea Hirudinea 3 1 8 0 0 0 1 0 1 1 0 0 7 Isopoda Asellidae 0 0 52 1 0 0 10 57 0 0 0 0 25 Megaloptera Sialidae 0 0 1 0 0 0 0 0 0 0 0 0 1 Oligochaeta Enchytraeidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbricidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lumbriculidae 0 0 0 0 0 0 4 0 0 0 0 0 0 Naidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Tubificidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Plecoptera Chloroperlidae 5 0 0 3 28 52 0 0 11 8 0 0 0 Leuctridae 6 0 2 5 3 10 7 12 5 3 0 0 0 Nemouridae 3 0 0 16 60 22 6 0 0 2 1 0 0 Perlidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Perlodidae 10 0 9 11 15 30 0 1 1 10 2 0 6 Taeiniopterygidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Trichoptera Glossosomatidae 5 25 12 1 4 4 0 0 30 52 60 197 23 Hydropsyche siltalai Döhler 14 13 0 14 71 55 80 235 0 5 0 0 0 Hydroptillidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Lepidostomatidae 10 1 0 0 0 7 15 25 0 0 0 0 0 Leptoceridae 0 2 0 0 0 0 0 0 1 4 3 0 7 Limnephillidae 18 1 4 5 11 10 48 34 4 12 5 13 16 Odontocerum albicorne Spence 0 0 0 0 0 0 0 0 0 0 0 0 0 Philopotamidae 1 2 0 1 0 1 0 0 1 7 0 0 0 Polycentropodidae 0 0 0 0 0 0 0 0 0 0 0 0 0 Psychoididae 7 0 6 5 15 2 0 0 8 11 1 0 9 Ryacophillidae 1 1 1 1 4 0 0 0 0 7 1 0 1 Sericostoma personatum Spence 0 0 0 0 0 2 0 0 3 1 2 2 13
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