ARTICLE IN PRESS
WAT E R R E S E A R C H
40 (2006) 91 – 98
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Linking catchment characteristics and water chemistry
with the ecological status of Irish rivers
Ian Donohuea,b, Martin L. McGarriglec,, Paul Millsd
a
Department of Zoology, Trinity College, University of Dublin, Dublin 2, Ireland
Centre for the Environment, Trinity College, University of Dublin, Dublin 2, Ireland
c
Environmental Protection Agency, John Moore Road, Castlebar, Co. Mayo, Ireland
d
Compass Informatics, 19 Grattan Street, Dublin 2, Ireland
b
art i cle info
A B S T R A C T
Article history:
Requirements of the EU Water Framework Directive for the introduction of ecological
Received 13 July 2004
quality objectives for surface waters and the stipulation that all surface waters in the EU
Received in revised form
must be of ‘good’ ecological status by 2015 necessitate a quantitative understanding of the
23 August 2005
linkages among catchment attributes, water chemistry and the ecological status of aquatic
Accepted 21 October 2005
ecosystems. Analysis of lotic ecological status, as indicated by an established biotic index
based primarily on benthic macroinvertebrate community structure, of 797 hydrologically
Keywords:
independent river sites located throughout Ireland showed highly significant inverse
Monitoring
associations between the ecological status of rivers and measures of catchment
Rivers
urbanisation and agricultural intensity, densities of humans and cattle and chemical
Nutrients
indicators of water quality. Stepwise logistic regression suggested that urbanisation, arable
Ecological quality
farming and extent of pasturelands are the principal factors impacting on the ecological
Risk
status of streams and rivers in Ireland and that the likelihood of a river site complying with
Catchment
the demands of the EU Water Framework Directive, and be of ‘good’ ecological status, can
be predicted with reasonable accuracy using simple models that utilise either widely
available landcover data or chemical monitoring data. Non-linear landcover and chemical
‘thresholds’ derived from these models provide a useful tool in the management of risk in
catchments, and suggest strongly that more careful planning of land use in Ireland is
essential in order to restore and maintain water quality as required by the Directive.
& 2005 Elsevier Ltd. All rights reserved.
1.
Introduction
Requirements of the EU Water Framework Directive (CEC,
2000) for the introduction of ecological quality objectives for
surface waters and the stipulation that all surface waters in
the EU must be of ‘good’ ecological status (on a five-point
index ranging from ‘high’ to ‘bad’) by 2015 necessitate an
increased and quantitative understanding of the linkages
among catchment attributes, water chemistry and the
ecological status of aquatic ecosystems. Morphological,
Corresponding author. Tel.: +353 94 9048430; fax: +353 94 9021934.
E-mail address: [email protected] (M.L. McGarrigle).
0043-1354/$ - see front matter & 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.watres.2005.10.027
geological and landcover attributes of catchments influence
the characteristics of lotic systems considerably, affecting
both chemical characteristics (Osborne and Wiley, 1988;
Johnes et al., 1996; Soranno et al., 1996; Johnson et al., 1997;
Donohue et al., 2005; Styles et al., in press) and biotic
community structure (Poff and Allan, 1995; Richards et al.,
1996; Sponseller et al., 2001; Townsend et al., 2004). Even
though it is well known that intensification of land use
through, for example, urbanisation (Jones and Clark, 1987;
Wang et al., 1997) or intensification of agriculture (Wang et al.,
ARTICLE IN PRESS
92
WAT E R R E S E A R C H
1997; Harding et al., 1999; Cuffney et al., 2000) tend to result in
significantly decreased ecological quality of aquatic networks,
the absence of widely applicable and empirically robust
landcover guidelines to ensure good ecological quality
hinders the effective and efficient management of these
systems. Relationships between ecological quality and water
chemistry are equally vague, in spite of the fact that chemical
monitoring has generally been the principal legislative tool
used for evaluating the integrity of aquatic systems for
decades.
Benthic macroinvertebrate assemblages have been used
widely to assess the ecological quality of streams and rivers
(Rosenberg and Resh, 1993), and are utilised frequently to
calculate simple indices which characterise ecological status
(Johnson et al., 1993). These indices can be as good as, or
better than, more quantitative and taxonomically rigorous
methods for measuring ecological quality (Hewlett, 2000;
Reynoldson et al., 2001; Metzeling et al., 2003; Waite et al.,
2004). In Ireland, the Quality Rating System (Flanagan and
Toner, 1972; McGarrigle et al., 2002) has been used to monitor
the ecological quality of streams and rivers since 1971, and is
the foundation of one of the longest-running national
biological monitoring programme for rivers in Europe. Over
3000 sites on some 13,200 km of main river channel are
included in the current national survey and assessed using
the Quality Rating System to characterise water quality. The
Quality Rating System is based principally on the structure of
benthic macroinvertebrate communities, but also takes into
consideration aquatic macrophytes and phytobenthos. There
are nine possible scores (Q-values) ranging from 1, indicative
of extremely poor ecological quality, to 5, indicative of
minimally impacted conditions. A recent assessment of the
ecological status of Irish rivers (McGarrigle et al., 2002) found
that 62.3% of 3166 sites surveyed were of ‘good’ status or
better (QX4), a figure which corresponds to 70% of the length
of 13,200 km of main-stem Irish river channel. Some 24% of
sites examined were of ‘high’ status with approximately 3%
being close to ‘reference’ status with minimal anthropogenic
disturbance. The Quality Rating System has been shown to be
a robust indicator of lotic water quality, and has been linked
strongly with both chemical status (Clabby et al., 1992;
McGarrigle et al., 1992; McGarrigle, 2001) and with the
structure of fish assemblages (Champ and Kelly, unpublished
data). The connection between Q-values and orthophosphate
concentrations in rivers has been used as the basis of national
legislation (DELG, 1998) that sets standards for phosphorus
concentrations in rivers under the EU Dangerous Substances
Directive (CEC, 1976) with a view to controlling eutrophication
in Irish waters. The Quality Rating System thus provides a
reasonable measure of ecological status having established
links with a number of specified elements in Annex V of the
Water Framework Directive and with physico-chemical status.
The goals for the work described in this paper are: (1) to
investigate the nature of relationships between the ecological
status of rivers in Ireland and catchment pressures such as
landcover type, human and livestock densities; (2) to explore
the associations between Q-values and water chemistry; (3) to
examine whether the likelihood that a river site will comply
with the demands of the Water Framework Directive can be
predicted reasonably accurately using catchment attributes
40 (2006) 91– 98
or water chemistry data; and (4) to derive meaningful
thresholds of landcover and water chemistry useful to
catchment managers.
2.
Methods
2.1.
Ecological status
The Irish Quality Rating System (Flanagan and Toner, 1972;
McGarrigle et al., 2002) was used as a surrogate for ecological
status sensu the Water Framework Directive (CEC, 2000). Single
Q-values from a subset of 797 hydrologically independent river
sites (i.e. no catchment is downstream of another already
included in the analysis), selected from a total of 2548 sites that
were monitored by the Irish Environmental Protection Agency
from 1999 to 2002, were analysed in this study. Single sites on
rivers that contained a number of monitoring sites were chosen
using random number tables. Selected sites were located
throughout Ireland (Fig. 1) and covered considerable variability
of both catchment attributes and water chemistry (Table 1).
2.2.
Water chemistry
Water chemistry data were available from 1999 to 2002 for a
total of 309 of the selected sites, with each of unfiltered
molybdate-reactive phosphorus (MRP, 308 sites), total ammonia (280 sites), unfiltered nitrate plus nitrite (144 sites),
Figure 1 – Locations of sampling sites included in this study,
classified as being of either ‘good’ (closed circles) or
‘not good’ (open circles) ecological status (from EPA
national monitoring data, 1999–2002).
ARTICLE IN PRESS
WAT E R R E S E A R C H
93
40 (2 006 ) 9 1 – 98
Table 1 – Ranges of selected catchment attributes and chemical variables analysed in this study, with Spearman Rank
correlation coefficients (rs), degrees of freedom (df) and statistical significance (p) of associations between these variables
and the Q-value
Range
rs
df
p
Sampling site elevation (m asl)
Slope of river stretch (%)
Net rainfall (mm a1)
0–280
0–32
252–2098
0.225
0.308
0.458
795
795
795
o0.0001
o0.0001
o0.0001
Physical catchment characteristics
Catchment area (km2)
Urban areas (%)
Forest cover (%)
Pasture (%)
Arable land (%)
Peat bogs (%)
0.2–362
0–100
0–60
0–100
0–96
0–100
0.051
0.338
0.193
0.423
0.348
0.433
795
795
795
795
795
795
0.15
o0.0001
o0.0001
o0.0001
o0.0001
o0.0001
Human and livestock densities
Cattle (no. km2)
Sheep (no. km2)
Humans (no. km2)
0–935
0–6582
0–3132
0.376
0.038
0.392
795
795
795
o0.0001
0.29
o0.0001
Chemical variables
MRP (mg L1)
Ammonia (mg L1)
Nitrate+nitrite (mg L1)
Dissolved oxygen (%)
BOD (mg O2 L1)
Suspended sediments (mg L1)
pH (median)
Conductivity (mS cm1)
2–609
5–1337
0.03–25
64–116
0.4–5.3
4–25
5.8–8.4
45–1029
0.587
0.543
0.526
0.335
0.442
0.37
0.219
0.557
306
278
142
256
276
51
288
242
o0.0001
o0.0001
o0.0001
o0.0001
o0.0001
0.006
0.0002
o0.0001
dissolved oxygen (258 sites), biological oxygen demand (BOD,
278 sites), suspended sediments (53 sites), pH (290 sites) and
conductivity (244 sites) quantified between 1 and 81 times in
the sampling period. Mean values of each determinand from
each sampling site recorded over the sampling period were
analysed in this study. Quantification of all chemical variables
was done following standard methods (Clesceri et al., 1998) by
a number of Irish laboratories, which conduct strict intercalibration procedures at least four times annually.
2.3.
Spatial analyses
Catchments upstream of each sampling point were delineated
using custom-written software and a digital elevation model (at
20 m resolution) covering the whole of the Republic of Ireland.
Landcover data (from CORINE 2000 satellite landcover maps),
human and livestock census data (provided by the Central
Statistics Office of Ireland) and long-term averaged net rainfall
data (provided by Met Éireann) were calculated for each
catchment. Slopes of river stretches were calculated by
comparison of the elevation at the sampling site with that
100 m upstream. All spatial analyses were done with ArcViews
Version 3.2, with the Spatial Analyst (Version 2.0) extension.
2.4.
‘good’ ecological status, as defined in Annex V of the Water
Framework Directive, and defined here as a Q-valueX4, was
modelled separately using catchment attribute (physical and
biotic) and river chemistry data with forward stepwise binary
logistic regression, using logit transformations. An initial
judgement from the official WFD intercalibration process
suggests that ‘reference’ sites will always have a Q-value of five,
while ‘high’ status sites may have a Q-value of 5 or 4.5. ‘Good’
status corresponds to a Q-value of 4. Initial unpublished
results from this process based on an analysis of many
thousands of samples also show very good correspondence
between Irish Q-values and a wide range of biological metrics
in common use in other European Union Member States. In
an attempt to keep models meaningful and maximise
parsimony, each model was limited a priori to a maximum
of three predictor variables. Odds ratios (ratios of the odds of
an event occurring in one group to the odds of it occurring in
another group) were derived, with confidence intervals, from
the logistic regression models. Non-linear (logarithmic and
quadratic) regression analyses were then used to model the
relationships between the variables included in the logistic
regression models and the estimated probability of attaining
‘good’ ecological status. All statistical analyses were done
with SPSSs Version 11.
Statistical methods
3.
Spearman Rank correlation tests were used to test for
associations between Q-values and both catchment attributes
and river chemistry. The probability that a river would attain
Results
Highly significant associations were found between the
Q-value and both physical and biotic attributes of catchments
ARTICLE IN PRESS
94
WAT E R R E S E A R C H
and river chemistry (Table 1). In particular, while the Q-value
was associated inversely with measures of catchment urbanisation and agricultural activity, densities of humans and
cattle and riverine concentrations of nutrients, positive
relationships were found with the proportion of forests and
peat bogs in catchments and the percent saturation of
dissolved oxygen in the water column.
The application of stepwise binary logistic regression
resulted in the formulation of two highly significant models
(Table 2); the model based on physical and biotic catchment
characteristics (w2 ¼ 182:4, df ¼ 3, po0:0001) included the
percent of urban area, pasture and arable land as explanatory
variables; while the model based on river chemistry data
(w2 ¼ 88:4, df ¼ 3, po0:0001) incorporated data on MRP and
ammonia concentrations and conductivity. Hosmer–Lemeshow goodness-of-fit tests found no significant differences
between observed and expected frequencies for either model
(w2 ¼ 6:55 and 9.74; p ¼ 0:59 and 0.28, respectively, for the
models based on catchment attributes and water chemistry;
df ¼ 8 in both cases). Although the proportion of the total
variance explained by each model was relatively low (pseudor2 ðNagelkerkeÞ ¼ 0:28 and 0.44, respectively), both models
predicted the ecological status of monitoring sites in the
original dataset correctly over 75% of the time (75.3% and 76%,
respectively). The application of both models to the complete
dataset from which the hydrologically independent subset
was drawn randomly, and excluding the data included in the
models, resulted in 72.7% correct predictions using the
catchment model and 76% using the model based on
chemical data.
The calculation of odds ratios from the models (Table 2)
suggest that, for every 10% decrease of urban area, pasture or
arable land in a catchment, the likelihood of a river being
assigned ‘good’ ecological status sensu the Water Framework
Directive is increased by, respectively, 85.1, 1.2 and 1.9 times.
Conversely, for every 10 mg L1 increase in the mean concentrations of MRP and ammonia, or 10 mS cm1 increase in
conductivity, this likelihood is reduced to, respectively, 0.83,
0.94 and 0.96 times. Further, relationships between each
variable included in the models and the estimated probability
of a river site being assigned ‘good’ ecological status as
predicted by the models (Fig. 2) support the possibility of
40 (2006) 91– 98
deriving meaningful landcover and chemical water quality
thresholds, with confidence intervals, from these data. We
attempted to derive these thresholds by setting the probability of a river site being assigned ‘good’ ecological status
equal to 0.75 in non-linear regression models, with each of
the catchment attributes and chemical variables included
in the logistic regression models as dependent variables
(Table 3).
4.
Discussion
The highly significant inverse associations between the value
of the Q-index and measures of pressures such as urbanisation and agricultural intensity, human and cattle densities
and chemical indicators of water quality support previous
findings (Clabby et al., 1992; McGarrigle et al., 1992; McGarrigle, 2001) that the Q-system provides a robust and sensitive
measure of riverine water quality. These relationships also
support further the use of the Quality Rating System as a
basis for the development of an ecological classification
system for Irish rivers suited to the needs of the EU Water
Framework Directive. The lack of association found between
Q-value and catchment area suggests that the Quality Rating
System is largely robust to changes in river size and relative
position of a sampling site along the river continuum (sensu
Vannote et al., 1980). Contrary to the situation in many more
heavily industrialised countries, where, generally speaking, it
is very difficult to find a large unpolluted river, there are a
number of relatively large catchments (1000 km2) of at least
‘good’ status in Ireland and indeed some of ‘high’ status such
as, for example, sections of the lower River Moy. Positive and
highly significant relationships found, however, between the
Q-value and catchment elevation, net rainfall and the slope of
the river stretch likely reflect generally lower anthropogenic
pressures at upland sites. For the determination of a Q-value
at a site, ecological status is gauged relative to the expected
reference conditions for that site. Hydromorphology is
obviously very important in determining the expected
reference communities at a given river site, and this is
accounted for in the calculation of the Q-value on a site by
site basis. Thus, the fact that a completely different macro-
Table 2 – Regression coefficients (b7s.e.), degrees of freedom (df), statistical significance (p) and odds ratios of being
assigned ‘good’ ecological status (for both an increase and a decrease of 10 units, with 95% confidence intervals) for the
variables included in the logistic regression models based on (a) catchment attributes and (b) river chemistry
Model
Variables included
b (7s.e.)
df
p
Odds ratios
10 unit increase
95% CI
10 unit decrease
95% CI
(a)
Urban area (%)
Pasture (%)
Arable land (%)
0.44470.097
0.01870.003
0.06270.013
1
1
1
o0.0001
o0.0001
o0.0001
0.01
0.83
0.54
0.002–0.08
0.79–0.88
0.42–0.69
85.1
1.2
1.86
12.78–566.48
1.13–1.27
1.45–2.37
(b)
MRP (mg L1)
Conductivity (mS cm1)
Ammonia (mg L1)
0.01970.007
0.00470.001
0.00670.003
1
1
1
0.005
o0.0001
0.05
0.83
0.96
0.94
0.73–0.95
0.95–0.98
0.89–1
1.2
1.04
1.06
1.06–1.37
1.02–1.05
1–1.13
ARTICLE IN PRESS
WAT E R R E S E A R C H
95
40 (2 006 ) 9 1 – 98
Figure 2 – Estimated probabilities of attaining ‘good’ ecological status sensu the EU Water Framework Directive (i.e. QvalueX4) for river sites varying in landcover and chemical attributes as predicted by the logistic regression models
based on (a) catchment characteristics and (b) river chemistry.
Table 3 – Results of non-linear regressions between the catchment attributes and chemical variables included in the
logistic regression models (see Table 2) and the estimated probability of attaining ‘good’ ecological status
Dependent variable
Urban area (%)
Pasture (%)
Arable land (%)
MRP (mg L1)
Conductivity (mS cm1)
Ammonia (mg L1)
Model
F
df
p
r2
Predicted
threshold
(probability 0.75)
95% CI
Log: y ¼ 0:5221:89 lnðxÞ
10200.9
1219.3
1795
2794
o0.0001
o0.0001
0.93
0.75
0.03%
37.7%
0–2.6%
6.7–68.7%
Quadratic: y ¼ 44:84 þ 186:81x2261:69x2
Quadratic: y ¼ 20:87237:79x þ 15:63x2
Log: y ¼ 12:6230:77 lnðxÞ
Quadratic: y ¼ 593:96 þ 356:67x21026:6x2
Log: y ¼ 7:57274:67 lnðxÞ
211.2
2794
o0.0001
0.35
1.3%
0–14.4%
961.2
404.6
1223
2222
o0.0001
o0.0001
0.81
0.79
21.5 mg L1
284 mS cm1
0–84.9 mg L1
71–500 mS cm1
645.3
1223
o0.0001
0.74
29.1 mg L1
0–216.6 mg L1
Predicted values of the dependent variables at probability 0.75 and their 95% confidence intervals are also shown.
invertebrate community would be expected, for example, in
an unpolluted, upland, acidic, highly sloping river site
compared with an unpolluted, lowland, calcareous river, is
accounted for in the Quality Rating System.
Somewhat surprisingly, the logistic regression model based
on physical and biotic catchment attributes incorporated only
landcover variables in preference to measures of human and
livestock population densities, which have been shown
ARTICLE IN PRESS
96
WAT E R R E S E A R C H
previously to be important drivers of diffuse water pollution
in Ireland (Allott et al., 1998; Irvine et al., 2000) and elsewhere
(Harding et al., 1999). This may be, in part, owing to
improvements in the interpretation of satellite imagery which
underpin the latest CORINE landcover maps used in this
study. In addition, there is inherent variability in the impact
of a given density of livestock or humans on water quality,
which is not accounted for directly in our ‘black-box’ models.
Sources of this variability include considerable diversity in
agricultural management practices, such as the size of the
phosphorus application surplus, the sufficiency of animal
manure storage capacity and the presence or absence of
intact riparian zones and spatial variability in the extent of
wastewater treatment. The interactions between these factors and morphological characteristics of catchments, such as
slope or soil type, increase further the spatiotemporal
variability and overall complexity not accounted for by the
models. In spite of this, our work has demonstrated that
relatively simple models based on easily obtainable data may
be used to predict the ecological status of rivers, or to identify
systems that are unlikely to comply with new legislation, to a
reasonable degree of accuracy. The inclusion of MRP and
ammonia concentrations in the model based on water
chemistry likely reflects their importance as the fractions of
phosphorus and nitrogen most amenable to uptake by algae
and macrophytes, as well as possible toxicity from organic
pollution in the case of ammonia. The incorporation of
conductivity in the model as an important predictor of
ecological status is likely owing both to its relationship with
intensity of catchment land use and as an indirect indicator
of the connectivity between land and water, rather than
having a direct impact on water quality per se. High
conductivity is an indicator of well-drained, more calcareous
and productive catchment soils, which are likely to support
more intensive farming practices and higher populations of
humans and livestock.
The non-linear ‘threshold’ models derived from the landuse logistic regression model had, in general, high predictive
power and give considerable insight into the catchment and
chemical attributes that are required to attain high lotic
ecological quality. Even with the probability of attaining
‘good’ ecological status set at a relatively lenient level of
0.75, however, the figures produced by the models contrast
somewhat with other published figures, in particular for the
extent of urban areas. Roy et al. (2003), for example, suggested
that it is only when urban areas comprise greater than 15% of
catchments that a reduction in water quality is ‘detectable’,
while Wang et al. (1997) proposed 10–20%, and Klein (1979)
10%. Although other authors advocated figures in the range
4.4–6% (Morse et al., 2003; Ourso and Frenzel, 2003), even
these remain considerably in excess of the ‘thresholds’
suggested by our landcover model (Table 3). It may be that
differing standards for what is regarded as acceptable water
quality account, at least in part, for these discrepancies, as
Irish water quality standards are relatively strict (annual
median orthophosphate concentrations 430 mg L1 are likely
to be classified as falling below the ‘moderate/good’ WFD
ecological status boundary) and are designed to protect
pollution-sensitive salmonid species. Further, likely differences between countries in the extent of wastewater treat-
40 (2006) 91– 98
ment and human population densities in what comprises an
‘urban’ area will also have contributed to the differences
between these figures. Regarding agricultural land uses, our
estimated ‘threshold’ of 38% for pasturelands is lower than
that of Wang et al. (1997), who found declines in lotic habitat
quality and biotic integrity when agricultural landcover
exceeded 50% of the catchment area, while some sites had
good ecological quality when agriculture exceeded even 80%.
The relatively broad 95% confidence interval for our estimate,
however, includes the 50% threshold of Wang et al. (1997), but
suggests strongly that rivers with greater than 69% agricultural landcover in their catchments would be unlikely to meet
the requirements of the Water Framework Directive in Ireland
without improved pollution control measures.
The Water Framework Directive requires that, for each type
of water body, biotic communities be compared with reference conditions found in unimpacted situations. Reference or
natural concentrations for soluble reactive phosphorus in
rivers are typically in the range 0–10 mg L1 and for total P in
the range p5–50 mg L1 (Kristensen and Hansen, 1994; Crouzet
et al., 1999; USEPA, 2000). Existing legislation in Ireland
effectively sets a 30 mg L1 standard for unfiltered MRP in
rivers, and 20 mg L1 for total P in lakes. Our results support
the validity of these standards. Similar phosphorus standards
have also been recommended internationally (OECD, 1982;
USEPA, 2000).
5.
Conclusions
Although a number of physical and biotic attributes of
catchments were associated highly significantly with Qvalues, our results suggest that urbanisation, arable farming
and extent of pasturelands are the principal pressures at the
catchment scale that impact on the ecological quality of
streams and rivers throughout Ireland. Further, our results
indicate that the likelihood of a river being at risk of failing to
achieve ‘good’ ecological status as required by the Water
Framework Directive can be predicted with simple models
utilising either widely available landcover data or chemical
monitoring data. The landcover and chemical recommendations derived from the probability distributions produced by
these models have the potential to be of use in the management of risk and as a planning tool in catchments.
Our results suggest strongly that, if current land uses
continue unchanged, it will be very difficult to meet the
demands of the Water Framework Directive. Measures to
bring about significant reductions in nutrient exports from
agriculture, for example, will be required, and, in general,
more careful planning of land use is needed in order to
restore and maintain water quality as required by the
Directive.
Acknowledgements
The authors wish to express their gratitude to the individuals
and organisations that provided water chemistry data, in
particular, to the Local Authorities laboratories and EPA
laboratories at Dublin, Kilkenny, Castlebar and Monaghan.
ARTICLE IN PRESS
WAT E R R E S E A R C H
We also wish to acknowledge the help of Met Éireann and the
Central Statistics Office of Ireland. This study was funded in
part under the Environmental Research Technological Development and Innovation Programme financed by the Irish
Government under the National Development Plan
(2000–2006) and administered on behalf of the Department
of the Environment and Local Government by the Environmental Protection Agency.
R E F E R E N C E S
Allott, N., Free, G., Irvine, K., Mills, P., Mullins, T.E., Bowman, J.J.,
Champ, W.S.T., Clabby, K.J., McGarrigle, M.L., 1998. Land use
and aquatic systems in the Republic of Ireland. In: Giller, P.S.
(Ed.), Studies in Irish Limnology. The Marine Institute Ireland,
pp. 1–18.
CEC, 1976. Directive 76/464/EEC of the European Parliament and of
the Council on pollution caused by certain dangerous substances discharged into the aquatic environment of the
Community. Off. J. Eur. Commun. L129.
CEC, 2000. Directive 2000/60/EC of the European Parliament and of
the Council: establishing a framework for Community action
in the field of water policy. Off. J. Eur. Commun. L327.
Clabby, K.J., Lucey, J., McGarrigle, M.L., Bowman, J.J., Flanagan, P.J.,
Toner, P.F., 1992. Water Quality in Ireland 1987–1990, Part One:
General Assessment. An Foras Forbartha, Dublin, Ireland.
Clesceri, L.S., Greenberg, A.E., Eaton, A.D., 1998. Standard
Methods for the Examination of Water and Wastewater.
American Public Health Association, American Water Works
Association and Water Environment Federation.
Crouzet, P., Leonard, J., Nixon, S., Rees, Y., Parr, W., Laffon, L.,
Bøgestrand, J., Kristensen, P., Lallana, C., Izzo, G., Bokn, T., Bak,
J., Lack, T.J., Thyssen, N. (Eds.), 1999. Nutrients in European
Ecosystems. European Environment Agency, Copenhagen.
Cuffney, T.F., Meador, M.R., Porter, S.D., Gurtz, M.E., 2000.
Responses of physical, chemical, and biological indicators of
water quality to a gradient of agricultural land use in the
Yakima River Basin, Washington. Environ. Monit. Assess. 64,
259–270.
DELG, 1998. Local Government (Water Pollution) Act 1977 (Water
Quality Standards for Phosphorus) Regulations. S.I. No. 258/
1998. Irish Government Publications, Dublin, Ireland.
Donohue, I., Styles, D., Coxon, C., Irvine, K., 2005. Importance of
spatial and temporal patterns for assessment of risk of diffuse
nutrient emissions to surface waters. J. Hydrol. 304, 183–192.
Flanagan, P.J., Toner, P.F., 1972. The National Survey of Irish Rivers:
A Report on Water Quality. An Foras Forbartha, Dublin,
Ireland.
Harding, J.S., Young, R.G., Hayes, J.W., Shearer, K.A., Stark, J.D.,
1999. Changes in agricultural intensity and river health along a
river continuum. Freshwater Biol. 42, 345–357.
Hewlett, R., 2000. Implications of taxonomic resolution and
sample habitat for stream classification at a broad geographic scale. J. N. Am. Benthol. Soc. 19, 352–361.
Irvine, K., Allott, N., Mills, P., Free, G., 2000. The use of empirical
relationships and nutrient export coefficients for predicting
phosphorus concentrations in Irish lakes. Verhand. Int.
Verein. Theor. Angew. Limnol. 27, 1127–1131.
Johnes, P., Moss, B., Phillips, G., 1996. The determination of total
nitrogen and total phosphorus concentrations in freshwaters
from land use, stock headage and population data: testing of a
model for use in conservation and water quality management.
Freshwater Biol. 36, 451–473.
Johnson, L.B., Richards, C., Host, G.E., Arthur, J.W., 1997. Landscape influences on water chemistry in midwestern stream
ecosystems. Freshwater Biol. 37, 193–208.
40 (2 006 ) 9 1 – 98
97
Johnson, R.K., Wiederholm, T., Rosenberg, D.M., 1993. Freshwater
biomonitoring using individual organisms, populations, and
species assemblages of benthic macroinvertebrates. In: Rosenberg, D.M., Resh, V.H. (Eds.), Freshwater Biomonitoring and
Benthic Macroinvertebrates. Chapman & Hall, New York,
pp. 40–158.
Jones, R.C., Clark, C.C., 1987. Impact of watershed urbanization
on stream insect communities. Water Resour. Bull. 23,
1047–1055.
Klein, R.D., 1979. Urbanization and stream quality impairment.
Water Resour. Bull. 15, 948–963.
Kristensen, P., Hansen, H.A. (Eds.), 1994. European Rivers and
Lakes: Assessment of their Environmental State. European
Environment Agency, Copenhagen.
McGarrigle, M.L., 2001. Eutrophication of Irish waters—from
science to legislation and management. Went Memorial
Lecture, Occasional Papers in Irish Science and Technology 24,
1–18.
McGarrigle, M.L., Lucey, J., Clabby, K.J., 1992. Biological assessment
of river water quality in Ireland. In: Newman, P.J., Piavaux,
M.A., Sweeting, R.A. (Eds.), River Water Quality: Ecological
Assessment and Control. Commission of the European Communities, Luxembourg, pp. 371–393.
McGarrigle, M.L., Bowman, J.J., Clabby, K.J., Lucey, J.,
Cunningham, P., MacCárthaigh, M., Keegan, M., Cantrell, B.,
Lehane, M., Clenaghan, C., Toner, P.F., 2002. Water Quality
in Ireland 1998–2000. Environmental Protection Agency,
Ireland.
Metzeling, L., Chessman, B., Hardwick, R., Wong, V., 2003. Rapid
assessment of rivers using macroinvertebrates: the role of
experience, and comparisons with quantitative methods.
Hydrobiologia 510, 39–52.
Morse, C.C., Huryn, A.D., Cronan, C., 2003. Impervious surface
area as a predictor of the effects of urbanization on stream
insect communities in Maine, USA. Environ. Monit. Assess. 89,
95–127.
OECD, 1982. Eutrophication of Waters: Monitoring, Assessment
and Control. OECD, Paris.
Osborne, L.L., Wiley, M.J., 1988. Empirical relationships between
land use cover and stream water quality in an agricultural
watershed. J. Environ. Manage. 26, 9–27.
Ourso, R.T., Frenzel, S.A., 2003. Identification of linear and
threshold responses in streams along a gradient of urbanization in Anchorage, Alaska. Hydrobiologia 501, 117–131.
Poff, N.L., Allan, J.D., 1995. Functional organization of stream fish
assemblages in relation to hydrological variability. Ecology 76,
606–627.
Reynoldson, T.B., Rosenberg, D.M., Resh, V.H., 2001. Comparison
of models predicting invertebrate assemblages for biomonitoring in the Fraser River catchment, British Columbia. Can. J.
Fish. Aquat. Sci. 58, 1395–1410.
Richards, C., Johnson, L.B., Host, G.E., 1996. Landscape-scale
influences on stream habitats and biota. Can. J. Fish. Aquat.
Sci. 53, 295–311.
Rosenberg, D.M., Resh, V.H. (Eds.), 1993. Freshwater Biomonitoring
and Benthic Macroinvertebrates. Chapman & Hall, New York.
Roy, A.H., Rosemond, A.D., Paul, M.J., Leigh, D.S., Wallace, J.B.,
2003. Stream macroinvertebrate response to catchment urbanisation (Georgia, USA). Freshwater Biol. 48, 329–346.
Soranno, P.A., Hubler, S.L., Carpenter, S.R., Lathrop, R.C., 1996.
Phosphorus loads to surface waters: a simple model to
account for spatial pattern of land use. Ecol. Appl. 6, 865–878.
Sponseller, R.A., Benfield, E.F., Valett, H.M., 2001. Relationships
between land use, spatial scale and stream macroinvertebrate
communities. Freshwater Biol. 46, 1409–1424.
Styles, D., Donohue, I., Coxon, C., Irvine, K., in press. Linking soil
phosphorus to water quality in the mask catchment of
ARTICLE IN PRESS
98
WAT E R R E S E A R C H
western Ireland through the analysis of moist soil samples.
Agric. Ecosystems Environ.
Townsend, C.R., Downes, B.J., Peacock, K., Arbuckle, C.J., 2004.
Scale and the detection of land-use effects on morphology,
vegetation and macroinvertebrate communities of grassland
streams. Freshwater Biol. 49, 448–462.
USEPA (Ed.), 2000. Ambient Water Quality Criteria Recommendations: Information Supporting the Development of State and
Tribal Nutrient Criteria for Rivers and Streams in Ecoregion
VII. EPA 822-B-00-018. US Environmental Protection Agency,
Washington, DC.
40 (2006) 91– 98
Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., Cushing, C.E., 1980. The river continuum concept. Can. J. Fish.
Aquat. Sci. 37, 130–137.
Waite, I.R., Herlihy, A.T., Larsen, D.P., Urquhart, N.S., Klemm, D.J.,
2004. The effects of macroinvertebrate taxonomic resolution
in large landscape bioassessments: an example from the MidAtlantic Highlands, USA. Freshwater Biol. 49, 474–489.
Wang, L.Z., Lyons, J., Kanehl, P., Gatti, R., 1997. Influences of
watershed land use on habitat quality and biotic integrity in
Wisconsin streams. Fisheries 22, 6–12.