north south shared aquatic resource (ns share)...lakes. the work was funded through the north south...
TRANSCRIPT
North South Shared Aquatic Resource (NS Share) River and Lake Macrophytes
Index Development
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) i
North South Shared Aquatic Resource (NS Share) Water Framework Directive A Directive establishing a new framework for Community action in the field of water policy (2000/60/EC) came into force in December 2000. This Water Framework Directive (WFD) rationalises and updates existing legislation and provides for water management on the basis of River Basin Districts (RBDs). The WFD was transposed into national law in Northern Ireland by the Water Environment (Water Framework Directive) Regulations (Northern Ireland) 2003 and in the Republic of Ireland by the European Communities (Water Policy) Regulations 2003. The primary objective of the WFD is to maintain the “high status” of waters where it exists, prevent deterioration in existing status of waters and to achieve at least “good status” in relation to all waters by 2015. NS Share Study Area NS Share is a cross border project and incorporates three River Basin Districts as set out in the joint North/South Consultation paper Managing our Shared Waters:
1. North Western International River Basin District (NWIRBD);
2. Neagh Bann International river Basin District (NBIRBD);
3. North Eastern River Basin District (NERBD).
The NW and NB are International River Basin Districts as they share their waters between Northern Ireland (NI) and Republic of Ireland (ROI). The NERBD is contained wholly within NI.
NS Share Project The overall objective of the project is to strengthen inter-regional capacity for environmental monitoring and management at the river basin district level, to improve public awareness and participation in water management issues, and to protect and enhance the aquatic environment and dependent ecosystems. The NS Share project aims to facilitate delivery of the objectives of the WFD within the project area between August 2004 and March 2008. The NS Share project is funded by the EU INTERREG IIIA Programme for Ireland / Northern Ireland. The Department of the Environment (NI) and the Department of the Environment, Heritage and Local Government (ROI) are implementing agents for the project. Donegal County Council is the project promoter. Technical support is proivded by the Environment and Heritage Service an agency within the Department of the Environment (NI), and the Environmental Protection Agency (ROI). RPS Consulting Engineers in association with Jennings O’Donovan are the principal consultants. Assistance was also provided by the Marine Institute, Central Fisheries Board, Geological survey Ireland, Geological survey Northern Ireland, Loughs Agency, North West Regional Fisheries Board, and Cavan, Leitrim, Longford, Louth, Meath, Monaghan, and Sligo County Councils. Project publications are available at www.nsshare.com/publications
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) ii
PREFACE
The work presented in this paper was carried out as part of the NS SHARE project, which is
funded by the European Union INTERREG IIIA programme for Ireland/Northern Ireland. The
implementing agents for the NS SHARE project are the Department of Environment (DOE),
Northern Ireland, and the Department of Environment Heritage and Local Government
(DEHLG), Republic of Ireland. Donegal County Council (DCC) is the project promoter.
All data, drawings, reports, documents, databases, software and coding, website and digital
media and publicity material produced as part of this project shall be the property of the
DOE/DEHLG who will use, reproduce and distribute same as they see fit.
The views expressed in this document are not necessarily those of DOE, DEHLG or DCC.
Their officers, services or agents accept no liability whatsoever for any loss or damage
arising from the interpretation or use of the information, or reliance on views contained
herein. This document does not purport to represent policy of any government.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) iii
Index Development River and Lake Macrophytes
Ian Dodkins and Brian Rippey
University of Ulster, Coleraine
N. Ireland
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 1
Introduction
For the fulfilment of the Water Framework Directive (Council of the European Communities
2000) within Ecoregion 17 (Ireland), methods of measuring ecological status had to be
developed for each biological element. This report details the development of an index to
determine the ecological effect of anthropogenic impact on aquatic macrophytes in rivers and
lakes. The work was funded through the North South Shared Aquatic Resource project (NS
SHARE) for The Irish Environmental Protection Agency and Northern Irelands Environment
and Heritage Service.
Method development focuses initially on macrophytes in rivers. The lake macrophyte index
incorporates the same principles as the rivers methods, although the metrics developed and
the field survey methods differ. Previous work by Dodkins led to the development of a
method called CBAS (Canonical correspondence analysis Based Assessment System)
producing accurate multivariate metrics which could be used to diagnose and measure
anthropogenic impacts (Dodkins 2003, Dodkins et al. 2003). For reasons detailed within the
report, this original version of CBAS was a large influence on the development of the final
CBAS version, recommended for macrophyte ecological assessment in Ecoregion 17.
The final output of the macrophyte index development are the NS-SHARE Methods Manuals
for (II) River Macrophytes and (III) Lake Macrophytes, produced as separate documents.
Please note, that the development report tracks the investigations carried out over more than
two years, and that the final method development is still undergoing iterative improvements.
Therefore, refer to the most current NS-SHARE Methods Manuals for the latest versions of
the Macrophyte Index Method.
Authors:
Ian Dodkins, Brian Rippey
University of Ulster, Coleraine, Northern Ireland, BT52 2SA
(contact: [email protected])
June 2007
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 2
Contents
Page
Introduction 1
Macrophyte Index Methods Review 3
Rivers:
Exploring changes to CBAS 48
Developing the new CBAS Model 64
Preliminary Macrophyte Survey Method (Rivers) 115
Ecological Quality Status Bands, and Errors 127
Validation of CBAS for Rivers 141
Lakes:
Preliminary Macrophyte Survey Method (Lakes) 172
Development of CBAS for Lakes 183
References 217
Recent Amendments to method development 225
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 3
Macrophyte Index Methods Review
1st March 2005
1. Introduction The purpose of this report is to review the conceptual basis of methods for assessing
ecological status of rivers and lakes using macrophytes. Previously developed biological
assessment methods and new ecological assessment methods will be described and
compared in order to establish which methodologies are likely to be best for fulfilling the
obligations of the Water Framework Directive (WFD) (Council of the European Communities
2000) within Ecoregion 17 (Northern Ireland and the Republic of Ireland).
The WFD defines ecological status as “an expression of the quality of the structure and
functioning of aquatic ecosystems associated with surface waters...”. Ecological status in the
WFD is determined by comparing a site with reference conditions, which have “no or only
very minor anthropogenic alteration”. This gives a clear concept of high ecological status.
However the value assigned to different aspects of ecological ‘structure and function’ are not
specified other than being measured as deviation in “the composition and abundance of
aquatic flora”. Measurement of deviation from high status will be dependent on the weight
given to the different aspects of structure and function and the assessment method chosen.
Thus ecological status remains a fundamentally subjective measure.
Biological assessment in the WFD is to determine ecological quality and not simply trophic
status or water quality. Where possible, the best ecological assessment method should be
selected through scientific justification and validation.
Initially a description of the difference between lake and river habitats is addressed, which is
followed by a description of potential methods. The discussion focuses on the selection of
the best method.
Habitats in Lakes and Rivers Lakes tend to have more floating and submerged species than rivers, with less
hydromorphological pressures and more stable sediment accumulation, and also require a
different survey methodology. The optimal ecological assessment method will therefore
necessarily be different in rivers and lakes.
CCA (Canonical Correspondence Analysis) was conducted on rivers and lakes data from
Northern Ireland (McElarney 2002, Dodkins 2003) to determine the most important variables
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 4
affecting the occurrence and abundance of macrophyte species. While the analyses vary
according to whether unimpacted or impacted sites are included, and on the characteristics
of the sites or survey area, these analyses indicate that the variables most likely to be
important in the distribution of macrophytes in lakes and rivers are:
Within lakes alkalinity is the dominating variable, although pH is also important. Lake area
may be related to a scale issue with monitoring, and shore exposure. Colour is related to
light transparency within the lake (often due to humic substances).
Within rivers hydromorphology plays a much larger role, reflected by substrate type, slope
and DO (which reflects flow conditions to a large extent). pH, alkalinity and colour are also
important, as in lakes. The importance of neighbouring wetlands for rivers may reflect the
ability of wetlands to act as a source of propagules to enable rapid recolonisation of the river
following flood disturbance. It may be sensible to visualise macrophytes in rivers as having
the same physiological response to water chemistry as macrophytes in lakes, but there also
being a superimposed, but very important element of hydromorphology. Variation in lake
hydrology may be detectable, though not as apparent as in rivers. Macrophytes in lakes and
rivers are affected by light availability, although shading from the bank is more pronounced in
rivers.
In summary, alkalinity and probably colour would be good variables for structuring a lake
typology. An optimal river typology has already been determined using alkalinity and slope
(Dodkins et al., submitted). Other variables to which macrophytes respond strongly, but
cannot be used in the typology will be the main impacts. These would be:
Rivers
1. Substrate (silt/boulders)
2. Shade (light)
3. Nitrate
4. pH
5. Slope
6. Alkalinity/conductivity
7. Dissolved oxygen (flow conditions)
8. Colour (peat)
9. Neighbouring land-use (esp. wetlands)
Lakes
1. Alkalinity/conductivity
2. pH
3. Total Phosphorous
4. Lake area
5. Colour
6. Altitude
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 5
for lakes:
1. acidification
2. trophic state
3. water transparency
for rivers:
1. hydromorphological alteration
2. trophic state
3. acidification
Note that, although pH would be a useful variable for developing a typology in both lakes and
rivers, it is not an optional factor in System B of the WFD.
2. Ecological Assessment Methods The ecological assessment methods that are described start with simple expert scores and
then progress to predictive modelling. Although assessment methods of ecological status
may differ in rivers and lakes, they will be presented together since there is some
transference of techniques between the two. The methods described are:
2.1 Expert Scores
2.1.1 Mean Trophic Rank (MTR)
2.1.2 The River Trophic Status Indicator (RTSI)
2.1.3 Groupement d’Intérêt Scientifique (GIS)
2.1.4 Landolt Index
2.2 USEPA Bioassessment
2.3 STAR/AQUEM Project
2.4 Swedish Environmental Quality Criteria (SEQC)
2.5 ECOFRAME (for shallow lakes)
2.6 Free’s Lake Multimetric Index
2.7 Macrophytoindication (MPhI)
2.8 Schaumburg’s Vegetation Tables
2.9 LEAFPACS
2.10 RIVPACS
2.11 CCA-based Assessment System (CBAS)
2.12 Artificial Intelligence (Bayesian Belief Networks)
2.1 Expert Scores Scores that represent the value of an ecological property (usually a trophic gradient) are
assigned by expert judgement to species. The scores from these indicator species are then
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 6
combined to produce an overall score that represents the ecological property at a site (e.g.
the trophic state).
Trophic rank systems have been developed in different countries to measure the extent of
eutrophication at a site. Although the expert scores may vary, the methods of combining
these scores are similar.
2.1.1 Mean Trophic Rank (MTR) The MTR system was developed by Nigel Holmes (Holmes et al. 1999). Macrophyte species
were judged by Nigel to be positively or negatively associated with eutrophication and given
scores between 1 and 10; lower scores for species associated with eutrophication. Around
100 species have scores within MTR.
Species are also assigned to a percentage species cover value (SCV) category. Over the
standard survey length (100m) a nine-band SCV is used. With large rivers the survey length
can instead be 500m, and then the five-band cover categories are used.
Table 2.1.1.1 Five and Nine-band Species Cover Values (SCV) within MTR
Scale A (for 500m survey) Scale C (for 100m survey)
1 < 0.1 % 1 < 0.1 %
2 0.1 - 1 % 2 0.1 - 1 %
3 1 - 5 % 3 1 - 2.5 %
4 5 - 10 % 4 2.5 - 5 %
5 > 50 % 5 5 - 10 %
6 10 - 25 %
7 25 - 50 %
8 50 - 75 %
9 > 75 %
The score for each species is multiplied by the SCV and the result for all the species at a site
is added. The total is then divided by the sum of the species cover values, to provide a mean
score. Finally this is multiplied by 10 to give values between 1 (high trophic status) and 100
(low trophic status).
10×=∑∑
i
ii
csc
MTR
where:
ci = species cover value category
si = indicator value
n = number of scoring species
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 7
The method is simple and the MTR value can be easily calculated in the field. MTR scores
are correlated with phosphate concentration in rivers (Dawson et al. 1999), although MTR
scores are purported to reflect eutrophication rather than any one single chemical
constituent. Kelly and Whitton (1998) note a personal communication from F.H. Dawson, that
there is an inflection point on a plot of MTR against P concentrations in sites throughout
England and Wales. This inflection point is around 1 mg/l P, which suggests that above this
concentration the MTR score may actually drop.
Since MTR does not include a measure of the reliability of the species as an indicator,
species that are unreliable have to be omitted. Thus poor performance of the MTR method
has been found in low species diversity upland streams, and may be due to insufficient
indicator species (Demars and Harper 1988).
The Environment Agency determined several limitations in the application of MTR (Dawson
et al. 1999):
1. MTR scores vary with survey timing and they are also affected by poor quality surveys.
2. A significant change in MTR score is considered to be 4 units or 15%. Significance was
derived from double the mean seasonal change in MTR score, and the mean inter-
surveyor variation.
3. MTR is affected by the physical character of a river and additional water quality
parameters (size, slope, substrate, underlying geology, altitude at source, water
chemistry). Thus it is best for downstream comparison with sites of a similar physical
habitat. It is recommended that shaded areas be avoided and comparisons between
physically dissimilar sites should not be made.
(Kelly and Whitton 1998) also note that Holmes recognised boat traffic and weed cutting as
confounding factors in the interpretation of MTR.
2.1.2 The River Trophic Status Indicator Model (RTSI) The River Trophic Status Indicator Model (Murphy and Ali 1998) uses functional attributes of
plants (mainly morphological traits) as well as species indicator values to predict the trophic
status of a river and associated channel systems. The model variant which combines
morphological traits measured in the field, and ranked plant functional group relationship to P
concentrations, performed slightly better than MTR when tested in Scottish rivers.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 8
2.1.3 Groupement d’Intérêt Scientifique (GIS) Groupement d’Intérêt Scientifique was developed for France by (Haury et al. 1996). It is
similar to MTR but utilises more indicator species and enables different scores to be
produced depending on whether presence/absence or cover is measured and whether only
fully aquatic or emergent and aquatic species are utilised. It also uses a 1 to 10 scoring
system of species, but utilises more (around 200) indicator taxa.
The calculation of GIS is the same as that of the MTR score, though with different cover
categories and without multiplying by 10. In addition there is an equation that can be used for
presence/absence data instead of species cover. A GIS score can be generated either for
fully aquatic species only or for a combination of aquatic and emergent species (inundated at
least 40 % of the year). As with MTR a banded cover scale is used rather than directly using
percentage abundance.
Table 2.1.3.1 Cover categories used in GIS
% cover in the zone referred to
GIS cover category
0 0
+ 0.5
< 5 1
5 to < 25 2
25 to < 50 3
50 to < 75 4
75 to 100 5
For abundance data the GIS value for a site is calculated as:
and for presence/absence data as :
where:
ci = cover category
si = indicator value
n = number of scoring species
ns
GIS i∑=
∑∑=
i
ii
csc
GIS
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 9
A correlation between the GIS score and combined concentrations of ammoniacal nitrate and
orthophosphate has been found such that GIS > 7 suggests combined concentrations of < 50
µg/l, 5-7 suggest combined concentrations between 50 and 100 µg/l and GIS < 5 suggests
combined concentrations between 100 and 150 µg/l.
2.1.4 Landolt Index Landolt (1977) developed a trophic index that is correlated with nitrate concentrations. It was
formalised for Swiss flora but it is possible to apply it to sub-alpine regions. Around 3,400
species are utilised, including fanerogames, pteridophytes, bryophytes and even lichens. As
with GIS it can be calculated for fully aquatic species only, or for a combination of aquatic
and emergent species, and it can be calculated with presence/absence or with abundance.
The equations for the calculations are identical to that of GIS. The Landolt scale for species
is from 1 to 5, with increasing nitrate concentration.
2.2 USEPA Bioassessment More than 90% of state water resource agencies in the USA use the multimetric approach
(Barbour and Yoder 2000) to assess biological integrity, a concept analogous to the WFD’s
ecological status. A workable, practical definition of biological integrity was necessary before
being able to determine criteria for compliance (Yoder and Rankin 1998). It was considered
unrealistic for the goal to be ‘ecosystems unperturbed by human activities’ or ‘conditions
existing prior to European settlement’ (Barbour and Yoder 2000). Therefore biological
integrity was defined as the ability of an aquatic ecosystem to support and maintain a
balanced, integrated, adaptive community of organisms having a species composition,
diversity, and functional organisation comparable to that of the natural habitats of a region
(Karr and Dudley 1981). The WFD is more stringent as it defines high ecological status sites
as having “no significant anthropogenic impact”.
It may not be feasible to develop spatial reference conditions in Ecoregion 17 without a
moderate amount of pragmatism within the early years of implementation of the WFD. For
example, 66% of Europe’s wetlands have been drained since the beginning of the 20th
century (Commission of the European Communities 1995) and, though these would
undoubtedly have a large effect on nutrient balances in rivers and lakes, it is likely to be too
costly to replace them. High ecological status within the WFD is more demanding than the
USEPA’s concept of biological integrity, however Member States only have to achieve good
status (a slight deviation from undisturbed conditions) within the WFD. Therefore, despite
slightly different objectives, it is considered that some of the methods or concepts used to
measure biological integrity may be transferable from the USA to Europe.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 10
(Barbour and Yoder 2000) suggest that ecological assessment methods should:
1. be anticipatory i.e. it should provide information for needs not yet determined
2. integrate the effects of different stressors and provide an overall measure of the
aggregate of stressors
3. produce information which is easy to translate to the public and water mangers
4. be cost-effective
5. enable the prioritisation of mitigation and protection efforts
6. integrate patterns and processes from individuals to ecosystem levels
The primary approach in the USA is to use an array of metrics, which individually provide
information, but can also be combined into an overall measure of ecological integrity. The
multimetric concept was first implemented with the Index of Biological Integrity (IBI) (Karr and
Dudley 1981). Macrophyte metrics do not feature in the IBI , but the concepts behind metric
development can equally apply to macrophytes.
(Barbour et al. 1995) state that metrics should be:
1. ecologically relevant to the biological assemblage and the program objectives
2. sensitive to stressors
3. provide a response which can be discriminated from natural variation
These points form the initial screening stage of the metrics, after which redundant metrics
can be removed. Transformation of metric scores may be necessary to produce a linear
response to perceived ecological change (such as pollutant concentration, level of habitat
disturbance). The USEPA usually use percentiles to determine deviation of metrics from
‘reference’ values, though this is not appropriate for the WFD. Ideally the range over which
the metrics operate effectively should be determined (thresholds). Also it should be
understood that metrics might respond differently in different river or lake types. Different
metrics should be standardised such that each metric works to the same scale and can be
appropriately amalgamated (usually through taking the mean metrics score or summing the
metrics scores). Within the USEPA management decisions are made based on the individual
examination of component metrics rather than on a single aggregated score (Barbour et al.
1995). However, within the WFD legal decisions will be made based on a final aggregated
score, and therefore there must be extreme caution in the approach taken to metric
combination.
(Barbour et al. 1995) lists four areas which metrics should cover in order to comprehensively
assess changes to ecological structure and function:
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 11
1. community structure (e.g. richness, abundance, dominance)
2. taxonomic composition (e.g. identity, sensetivity, rare or endangered taxa)
3. individual condition (e.g. disease, anomolies, contaminant levels, metabolic rates)
4. biological processes (e.g. trophic dynamics, productivity, predation rate, recruitment
rate)
(Gray 1989) states that the three best-documented responses to environmental stressors
are:
1. reduction in species richness
2. change in species composition to dominance by opportunistic species
3. reduction in mean size of organisms (or changes in morphology, such as leaf size, for
macrophytes).
Most of the metrics in the IBI are simple and relate to the occurrence of different functional
groups of invertebrates or fish. Below is an example of some of the metric groups, with some
of their component metrics, within the IBI.
(Simon and Lyons 1995)
Species richness and competition Number of sunfish species
Number of darter species
Total number of fish species
Indicator species metrics Presence of salmonid species
Percent of native species
Percent of ‘tolerant’ species
Trophic function metrics Percent of generalised feeders
Percent of insectivores
Percent of large piscivores
Percent biomass of top carnivores
Abundance and condition metrics Biomass of fish
Biomass of amphibians
Density of macroinvertebrates
Percent of individuals with heavy infestation of cysts
Reproductive function metrics Percent of individuals that are gravel spawners
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 12
There are 79 metrics in the original version of the IBI. In contrast to metrics that utilise
predetermined tolerance values for species, such as MTR or GIS, most US metrics simply
measure very simple characteristics of the biology with no additional level of interpretation.
Some metrics that utilise tolerance values have been developed in the US, e.g. the
Invertebrate Community Index (ICI) (Deshon 1995).
Since interpretation of metric responses in the US involves examining a suite of different
metrics, a method of presenting and assessing the metrics is required. (Yoder and Rankin
1995, Barbour and Yoder 2000) illustrate this using metric response signatures.
(Johnson 2001) highlighted the problem of errors in metrics not being suitably quantified, and
where they are, error rates being high. In a study (Johnson 1999) he found a frequency of
10-23% of false positives i.e. impacts indicated where there were none (type I error) and 27-
55% of false negatives i.e. impacts not detected when it was occurring (type II error). This is
not too important where the complete metric response is interpreted before decisions on
ecological integrity are made, however this could have disastrous consequences within the
WFD. If Type II error shows deterioration in a water-body where there is none, money will be
unjustifiably invested in rehabilitating a water body. Fines could even be directed
inappropriately to water users, which may end up in a detailed legal dispute. It is therefore
important to ensure that, when a water-body is determined to be below good status, or
determined to have deteriorated, that there is sufficient confidence in the measurement of
ecological status. Sufficient confidence in the results is also a condition set within the WFD. I
would suggest that type II error is more deleterious than type I error (where there is an
impact, but it was not detected) within the WFD. This illustrates a conflict between the early
warning capability of biological monitoring, which is desirable for water management, and the
confident measurement of ecological status, which is required in the WFD.
2.3 STAR/AQUEM project Through the European ‘STAndardisation of River classifications’ (STAR) consortium, and the
previous AQUEM (Assessment of ecological QUality of rivers throughout Europe using
Macroinvertebrates) consortium, recommendations for developing and applying multimetric
indices have been developed. The consortium consists of members in the United Kingdom,
Austria, Czech Republic, Denmark, France, Germany, Greece, Italy, The Netherlands,
Portugal, Sweden, Latvia, Poland and the Slovak Republic. These Member States are not
obliged to utilise the methods developed, although the draft CEN standards for designing
multimetric indices follow the same approach (CEN 2004).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 13
STAR suggests using several metrics, which are then combined together to produce a final
ecological score that can be compared to reference conditions. The benefit of a multi-metric
approach is that by combining different categories of metrics (e.g. taxa richness, sensitive vs
tolerant spp, trophic structure) the assessment is more reliable than examining a single
attribute.
The following metric types are distinguished by STAR:
1. Composition/abundance. Metrics providing an abundance of a taxon or taxonomic
group e.g. % Potamogeton pectinatus
2. Richness/diversity. Metrics providing the number of taxa within a certain taxon or
any diversity index e.g. Shannon-Weiner, Margalef, Simpson indices or number of
taxa.
3. Sensitivity/tolerance. Metrics providing a ratio of sensitive to insensitive taxa for a
particular stress e.g. eutrophication.
4. Functional. Species traits, ecological guilds, feeding types or body-size.
Combining metrics The method of combining metrics will have a large effect on the final ecological score
produced. The STAR project suggests two approaches. In the ‘general approach’ metrics are
individually compared with reference conditions to produce scores which are then added or
averaged to produce an Ecological Quality Ratio (EQR). Within the ‘stressor-specific
approach’ metrics for a specific environmental stress (e.g. organic pollution or stream
morphology) are first combined, and then the results from each of the stressor categories are
compared with reference conditions to produce scores that are added or averaged, resulting
in the EQR. This approach provides useful information at three different levels: individual
metric results, stressor specific results, and the EQR.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 14
Figure 2.3.1 The “general approach” of multimetric assessment (from draft standards (CEN 2003b))
Figure 2.3.2 The “stressor-specific approach” of multimetric assessment (from draft standards (CEN 2003b))
metric
taxa
metric
metric
metric
metric
score
score
score
score
score
Ecological Quality Ratio
reference condition
}
Ecological Quality Ratio
} }
quality class module “organic
pollution”
quality class module “stream
morphology degradation”
metric
taxa
metric
metric
metric
metric
score
score
score
score
score
reference condition
}
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 15
Within the stressor specific approach the different stresses under which metrics could be
amalgamated are suggested to be:
1. organic pollution: sewage input
2. eutrophication: nutrient input i.e. non-point source
3. acid stress: permanent or temporary human-induced acidification
4. degradation in stream morphology: bed and bank alteration, habitat degradation,
landuse, straightening, migration barriers, siltation.
5. hydrological stress: flow regime, e.g. residual flow or pulse releases.
6. general degradation: simultaneous and inseparable impacts from more than one
stressor.
CEN draft standards and the STAR project recommend that metrics are validated by testing
whether there is a significant correlation between the metric and the stressor gradient using a
t-test, U-test or rank correlation (for quality classes) or Pearson’s r or Spearman’s r for a
continuous gradient. To remove redundant metrics a Spearman’s r or Pearson’s r correlation
matrix of all the metrics is produced. If R > 0.8 one of the metrics must be excluded. The
metric range is calibrated to a value between one and zero based on the upper and lower
percentiles of the metric response (5 or 10%). Metrics can be weighted to give more value to
those that are based on the whole community rather than single taxa.
Further details on the STAR project can be found on the web at: http://www.eu-star.at
Multi-metrics derived using this method will have the same problems found with metrics in
the USEPA approach, most importantly the high rates of type I and II error. The method of
metric combination does not alleviate this error, and final EQRs will depend on the selection
and number of metrics chosen rather than on any objective measure ecological status. (Suter
1993, Polls 1994, Milner and Oswood 2000) have all stated that multi-metrics which are
amalgamated inappropriately into a final score have little real meaning.
2.4 Swedish Environmental Quality Criteria (SEQC) The SEQC is based on two metric values, the number of species at a site and the Trophic
Ranking Score (TRS) derived by Palmer (1992). Rather than just adding the scores together,
these two metrics are used in conjunction to determine the status between 1 (best) and 5
(worst) as follows:
1. the number of species AND indicator ratio are equal to the reference value
2. number of species OR indicator ratio deviate from the reference value
3. number of species AND indicator ratio deviate from the reference value
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 16
4. number of species AND indicator ratio deviate from the reference value AND one deviates
greatly
5. > 75% of vegetation foreign to that like type or surface is completely overgrown with
elodeids/free-floating or emergent plants.
It is not assumed that low species numbers or high TRS necessarily indicate an impact, but
that a deviation from the range of expected numbers of species or TRS indicates an impact.
Thus a unidirectional relationship of the metric (trophic rank or species diversity) with the
impact gradient is not essential. This is useful since it is likely that species diversity initially
increases with nutrient enrichment, and then decreases (Haury 1996, Thiebaut and Muller
1998). The combination of the two metrics to produce a score is also appealing since it
places low value on a deviation of a single metric, which may actually be due to natural
variation. However the TRS may not be applicable for use within the WFD since it is highly
correlated with alkalinity, and may not reflect anthropogenic nutrient enrichment (personal
comunication. Nigel Willby).
2.5 ECOFRAME (for shallow lakes) In ECOFRAME it was considered that measures of ecological quality are not absolute, but
matters of judgement (Moss et al. 2003). Existing biological monitoring schemes throughout
Europe, developed to measure water quality, were not considered adequate for measuring
ecological status. It was also considered that distinct macrophyte communities could not be
specified for different lake types or ecological quality classes.
The ECOFRAME project developed a series of metrics covering chemical,
hydromorphological and biological elements for shallow lakes. The suggested metrics for
macrophytes, based on a survey of approximately 10% of the lake area are:
1. Number of submerged vascular plants
2. Number of floating vascular plants
3. Invasive exotics (number or abundance)
4. An abundance measure from sampling with a grapnel or double-headed rake
(0=none, 1=sparse plants, 2= up to 70% of rakes produce plant samples, 3>70% of
rakes produce plant samples).
5. Plant diversity (number of species)
6. Dominance of different plant communities (i. algae or bryophytes, ii. isoetids, iii.
charophytes, iv. sphagnum, v. elodeids and pondweeds, vi. nymphaeids or poorly
rooting canopy spp such as Ceratophyllum spp and Lemna trisulca).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 17
Each metric (referred to as a variable by Moss et al (2003)) is compared to reference
conditions for that metric and assigned individually to a status band (high to bad). This is a
simple method of transforming the metrics to the same scale and allows different status
boundaries for different metrics. Due to natural variation, it is unlikely that even pristine lakes
will achieve high status for every metric. Therefore a probabilistic approach is taken. Based
on the distribution of metrics occurring within each status class, the overall ecological status
is calculated. Initially the conditions for high status are assessed. If the criteria are not met
the conditions for moderate status are assessed etc.
Table 2.5.1 Criteria for achieving ecological status
Overall
ecological
status
Condition of attainment
high 80 % of the biological metrics are high status
good 80% of the biological metrics are at high or good status
moderate 80% of the biological metrics are at high, good or moderate status
poor 80% of the biological metrics are at high, good, moderate or poor
status
bad The sum of high, good, moderate and poor status metrics is less
than 80% of total
Although this method reduces the effect of natural variation, sensitivity may also be reduced.
For example, several metrics (up to 20%) could have bad status whilst the lake still achieves
high overall status. Striking the correct balance between sensitivity and robustness is likely to
be a key feature of any adopted ecological assessment method.
2.6 Free’s Lake Multimetric Index (from draft report for EPA)
A preliminary multi-metric index has been suggested by Gary Free for measuring ecological
status of lakes in Ireland. Metrics were generated from data in 159 lakes in the Republic of
Ireland, following CEN draft guidelines for metric development (CEN 2004).
A series of metrics were identified for lakes, which increase as total phosphate concentration
(TP) increases, without having an inflexion point (i.e. unidirectional) but had a linear or log-
linear response. Some of the metrics were selected from (Nichols et al. 2000).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 18
The list of metrics chosen were:
1. maximum depth of colonisation
2. relative frequency of Elodeids (functional group)
3. mean depth of macrophyte presence
4. relative frequency of tolerant taxa
5. relevant frequency of Chara (only for lakes > 100 mg/l CaCO3)
6. a plant trophic score based on TP concentration
The plant trophic score was calculated by (univariate) weighted averaging against TP
concentration. The score had a slightly higher correlation with TP (r = 0.70) than did (Palmer
et al. 1992) Trophic Rank Score (r = 0.61). In lakes with alkalinity above 20 mg/l Free’s score
was much more correlated with TP (r = 0.61) than the TRS (r = 0.25).
The six metrics were scaled from 0.1 (low status) to 1 (high status) and averaged to give a
macrophyte index score. To determine if the index was correlated with trophic status an Non
Metric Multidimensional Scaling of fourth root transformed species abundance was
conducted. The ordination was rotated to maximise the correlation between the first axis and
log TP. The multi-metric index was then plotted against the site scores along this axis to
produce a reasonable correlation of r2 = 0.50. However this correlation could still be due in
large to alkalinity since TP and alkalinity are highly correlated in lakes.
Averaging or addition of metrics presumes that each metric is given the same weight. Step-
wise multiple regression of the scores against the species is suggested as an alternative to
simple averaging or addition of metric scores. Each metric is thus scaled by the variance it
causes in the species data. Redundant metrics are automatically excluded. However, it does
not eliminate the problem of correlations between metrics, nor does it improve the metrics
that may have poor underlying gradients.
Four of the metrics (1,2,3 and 5) seem to have a strong relationship with water transparency.
This is functionally important, and the correlation between metrics (in all cases but one) is not
high enough to warrant exclusion (>0.8), however the metrics should not be weighted too
heavily towards one functional aspect of the lake. (Irvine et al. 2002) pointed out that the
range of colour evident in Irish lakes may make an apparently straightforward relationship
between depth of growth and light penetration too difficult to interpret within any practically
useful typology. This requires further investigation.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 19
2.7 Macrophytoindication (MPhI) Macrophytoindication (Rejewski 1981, Ciecierska 1997, Rejewski and Ciecierska 2004) was
developed for Polish lakes to distinguish anthropogenic impacts from natural lake
succession. This is achieved by assuming that natural succession results in more complex
community structures whilst ‘synanthopization’ results in simple ones. (Ciecierska 2004)
suggests that structural and spatial changes will be the best descriptor of anthropogenic
change in Poland.
Distinct macrophyte communities were first defined for the ecoregion. Within Eastern Europe
the term phytocenosis is used to mean a spatially explicit community. Successional age of
the lake and anthropogenic impact is determined by collecting the following data at each
lake:
1. area covered by each community
2. total area in which plants have sufficient light with which to grow (the phytolittoral
area)
3. the total number of plant communities represented at the lake
4. the water area within the 2.5m isobath
Calculating the developmental age of a lake From these characteristics a measure of the proportion of colonisable area actually colonised
(colonisation index) and a measure of the community diversity (phytocenotic diversity index)
are calculated. These two metrics are multiplied together to produce the ‘succession
product’, which represents the developmental age of the lake. i.e. the more area colonised
and the more diverse the community the greater the developmental age.
1. The phytocenotic (community) diversity index (D) is calculated as follows:
∑ ⎟⎠⎞
⎜⎝⎛ ×−=
Aa
AaD ii ln
where:
ai = area covered by a given plant community
A = phytolittoral area
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 20
2. The colonisation index (C) is calculated as a ratio between the phytolittoral area (A) and
the water surface area limited by the 2.5 m isobath (izob2.5):
5.2izobAC =
3. The succession product (S), reflecting the developmental ‘age’ of a lake is then
determined as a product of the phytocenotic diversity index and the colonisation index.
CDS ×=
Anthropogenic Impact (ESMI) The ecological state macrophyte index (ESMI) reflects structural changes in lake vegetation
caused by anthropogenic activity. It utilises a ratio between the developmental age
(succession product) and the maximum potential structural development.
1. Maximum potential structural development (Dmax) is determined as the maximum value of
the phytocenotic index which is reached when all plant communities forming the littoral zone
are co-dominants i.e. occupy the same area i.e.
nD lnmax =
where n = the number of plant communities forming the phytolittoral.
2. ESMI, reflects the structural changes in the lake caused by anthropogenic activity, is
calculated using the ratio of the developmental age of the lake (succession product: S) to the
maximum potential structural development (Dmax):
⎟⎟⎠
⎞⎜⎜⎝
⎛ −
−= max1 DS
eESMI
Where:
e = 2.718 (base of the natural log)
S = the succession product
Dmax = the maximum value of the phytocenotic index i.e. ln (number of plant communities)
Thus the less the developmental age of the lake (relative to its maximum potential) the more
likely it is to have suffered anthropogenic impact.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 21
Table 2.7.1 Lake classification based on developmental age (Rejewski 1981)
Lakes groups Index of
Phytocenotic
diversity (D)
Colonisation
Index (C)
Succession product (S)
Very young
lakes
0.5 - 1.5 > 2.0 > 3.0
Young lakes 1.6 - 2.0 1.5 - 2.0 ± 3.0
Mature lakes > 2.0 (0.3) 0.5 - 1.5 > 0.5
Ageing lakes 1.5 - 2.0 (0.3) 0.5 - 1.5 0.5 - 1.5
Old lakes < 1.4 < 1.5 0.5 - 1.5
Table 2.7.2 Lake classification according to ecological state (Rejewski 1981, Ciecierska
1997)
Lake
groups
Ecological State Macrophyte Index (ESMI)
Very good 1.00 - 0.60
Good 0.60 - 0.40
Moderate 0.40 - 0.30
Sufficient 0.30 - 0.20
Bad 0.20 - 0.00
This method focuses on the cover of macrophytes within the lake. It does not evaluate
changes in species composition, although changes in ratios of defined communities will
affect the score.
2.8 Schaumberg’s Vegetation Tables (from Bavarian Water Management Agency (Schaumburg et al., 2005b; Schaumburg et al., 2005a))
For each lake type species are designated as having high abundance under reference
conditions, having high abundance under non-reference conditions or having no preference.
A score is generated based on a comparison between the occurrence of reference and
impact species. This score is then compared with the score expected at reference conditions
for that lake type.
Determining which are reference, impact and non-discriminatory species Firstly a vegetation table was created for each lake type. The reference sites within that lake
type were listed at the top of the table, with other sites (with a range of impacts) placed
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 22
below. Species are ordered across the top so that species which occur mainly at reference
sites are towards the left of the table. All other sites and species are arranged in the table
according to their similarity to the species composition at reference sites. Thus lakes are
sorted by their deviation in species composition from the reference sites.
The table was then divided into three groups, from left to right. Group A contains taxa with
high abundance under reference conditions and low or no abundance under non-reference
conditions (reference species). Group B taxa show no preference to reference or non-
reference conditions, occurring together with taxa from groups A and C (non-discriminatory
species). Group C contains species rarely found under reference conditions i.e. rarely found
with Group A taxa (impact species).
Groups A, B and C were confirmed using the literature. Rare taxa were included in the
analyses so that endangered species were not neglected. Vegetation tables were used to
define biocenoses (spatially defined communities).
Calculation of ecological quality A Reference Index (measure of ecological quality) was then generated by comparing the
total abundances of the three groups of species at a site, using this equation:
100*
1
1 1
∑
∑ ∑
=
= =
−=
G
A C
n
igi
n
i
n
iCiAi
Q
QQRI
Where:
RI = Reference Index
QAi = Abundance of the i-th taxon of species Group A
QCi = Abundance of the i-th taxon of species Group C
Qgi = Abundance of the i-th taxon of all Groups
nA = Total number of taxa of species Group A
nC = Total number of taxa of species Group C
ng = Total number of taxa of all Groups
The resultant values range from 100 (only species of group A) to -100 (only species of group
C). The range of RI values occurring at reference sites is defined as the acceptable range for
high ecological quality, and other status class boundaries were based on judgement.
Ecological assessment was considered unreliable if the list of indicative species does not
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 23
represent at least 75% of the total plant quantity at a lake (or 55% of total plant quantity for
lakes in mountainous regions or low alkalinity lakes, i.e. naturally species poor lakes).
This method follows the definition for deviation from ecological status outlined in the WFD in
a transparent and easily justifiable way. The success of this method is highly dependent on
the accurate designation of reference and impact species, and thus on the accurate
designation of reference conditions and the selection of impacted sites. The method provides
little diagnostic capability and relies heavily on subjective judgement. More investigation is
required in order to determine whether impacts other than nutrient enrichment can be
detected with this method. More investigation would be required to justify this method with
actual lake data.
2.9 LEAFPACS (summarised from an internal Environment Agency report by Nigel Willby,
June 2004) The LEAFPACS method was developed as an attempt to overcome the problems intrinsic in
expert-based metrics and in modelling methods using empirical data, by combining elements
of both.
Expert-based approaches, where species are scored by experts according to a subjective
assessment of their sensitivity to increasing nutrient status (or other impact), is common in
macrophyte assessment systems e.g MTR. Two problems with expert-based approaches
were identified:
1. The scores can be highly covariant with other variables such as alkalinity, so an
elevated score may not always indicate an impact.
2. Scores are usually based on observation rather than recorded environmental data
and therefore there is a large element of subjectivity. Since there is no accurate
definition of how the expert arrives at a score, and because the scores do not relate
directly to a single property of the environment, the scores are unfalsifiable. For
example, with MTR, although correlations between the MTR score and TP can be
calculated, the strength of correlation does not prove or disprove the value of the
index because TP is only one aspect of the pressure (trophic status) that the index is
intended to detect.
An alternative to expert-based approaches is the use of empirical data to create a model, for
example deriving optima from species and environmental data. Two problems with using
empirical data are:
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 24
1. Require high quality paired biological and environmental data (not necessarily the
case in the LEAFPACS data set)
2. By focusing on a single aspect of pressure e.g. TP, other pressure aspects that may
be more important in regulating the response may be ignored (e.g. pulses of TP,
nitrate or actual loading values)
The LEAFPACS method scales expert-based trophic scores to species-turnover units and
adjusts the scores based on species co-occurrence using DCA (Detrended Correspondence
Analysis). CCA is then utilised to remove the covariance between the trophic score and
alkalinity. These scores are then used to produce a list of reference and impact indicator
species, the ratio of which is used to determine the EQR.
Stage 1 - Calibration of expert scores It is assumed that the rank of species in expert-based systems is broadly correct but that
they may not always adequately reflect the underlying structure of the biological data, and
therefore they should be retuned. The calibration approach is similar to the revision of BMWP
scores for invertebrates by Walley and Hawkes (2001) and the reciprocal averaging method
used for refining Ellenberg Indicator Scores (Hill et al. 2000). Scores were amalgamated from
British, French and Swedish trophic ranking systems, and the Ellenberg scores revised by
Hill (2000).
Procedure:
1. Species trophic scores are determined from the expert systems (scaled as 1-10)
2. DCA of species data is conducted rescaling the first and recording species scores
along this axis.
3. The mid point between the expert-based score and the score on the first axis
determines the new rank assigned to a species, though the score on the first axis is
weighted by the frequency of the species in the test data set. Thus for rare species in
the original data set (where ordination would give a poor estimate of the optima) the
expert-based ranks are minimally modified, whereas for common species the score
largely follows the DCA axis score.
The benefits of this approach are that scores can be generated for rare species and other
species without scores (e.g. charophytes and bryophytes in lakes) through their co-
occurrence with scored species.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 25
A regression between TP concentration and the new scores gave an r2 value of 27%
compared to 18% using the TRS scores. Improvements at moderate to high alkalinity were
most marked. However, this correlation is not a true audit of the success of the index, since it
is supposed to represent more than TP concentration. The score thus remains unfalsifiable.
Stage 2 - Identifying indicators of impact The trophic scores for macrophytes in lakes are correlated with alkalinity. Thus, even within a
single alkalinity class of the lake typology, a difference between trophic score at the site and
within the reference conditions may be due to alkalinity and not impacts. Thus covariation
with alkalinity was removed.
Procedure:
1. Alkalinity classes are defined within the typology. The following procedure is
conducted separately for each lake type (enabling the different level of response to
alkalinity within each lake type to be characterised and removed).
2. CCA is used with species data, with the site fertility score (trophic score) as the
environmental variable (and thus axis 1), and alkalinity as a covariable.
3. Species that have a low species score on the axis will tend to be reliable indicators of
reference conditions (reference species). Species with high species scores on the
axis will tend to be reliable indicators of impacted conditions (impact species), and
species near the centre of the ordination are considered to be non-discriminatory
species.
‘Impact’ species would be expected to naturally form part of the assemblage of high status
sites, and it is only when they become abundant that the sites would be considered to be
impacted. A ratio of impacted to reference species is used instead of averaging the species
scores for a site, reducing the effect of natural variation on the score.
Stage 3 - Setting class boundaries The relative abundance of impact and reference species is determined and used to set class
boundaries. For example, the point at which cover of impact species exceeds the cover of
reference species could be the centre of the good status class, and the point at which there
are no reference species could be the middle of poor status. Standard error values can be
used to determine the boundaries between the different ecological status classes.
This method is still under development, but problems with this current version are:
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 26
1. In the DCA the assumption is that the 1st axis is due to trophic status. It is likely that the
first axis is actually more heavily correlated with alkalinity.
2. Using alkalinity as a co-variable in the CCA removes co-variance between trophy and
alkalinity, thus removing part of the trophic score response. Removal of co-variance is not a
good method of separating an impact gradient from a natural gradient since the larger the
problem covariation causes, the more the impact signal will be removed by using the natural
variable as a covariable. Instead reference conditions should be used to set a base-line of
trophic status for a specific alkalinity.
2.10 RIVPACS The River InVertebrate Prediction And Classification System (Moss et al. 1987) is used in the
UK to predict invertebrate species at a site, given no trophic impact. RIVPACS classifies
reference sites by species using TWINSPAN into river types. Multiple Discriminant Analysis
is used to define the location of these river types in ordination space based on a set of
environmental variables, including distance from source, mean substratum, mean water
width, altitude, discharge category, mean water depth, latitude and longitude. These
environmental variables are used to locate a monitoring site within this ordination space, and
thus between the different reference river types. Using the distance of the monitoring site to
the reference river types (and the species of which they are composed) the probability of
different species occurring at the monitoring site can be calculated. The results of this can be
used in a variety of ways. For example the ratio of observed to expected (O/E) numbers of
species can be determined as well as observed to expected BMWP scores (Hawkes 1998).
For the purposes of the WFD, the species predictions are useful for representing the
reference conditions. Ecological change can be measured by calculating a similarity
coefficient between the species predicted at reference conditions and the species actually
found at the monitoring site. A particular advantage of this method is that reference
conditions are interpolated, and not derived from a fixed typology (such as System A in the
WFD). Within a fixed typology the artificial structuring of the environment suggests a sharp
boundary change between one lake or river type and the next, which is almost always
unrealistic. Interpolation allows reference conditions to be determined more precisely, and
thus the distinction of disturbance from natural variation is more easily made.
The RIVPACS approach has been applied to macrophytes (Dodkins 2003, Dodkins et al.
2003), but with very little success. There were several problems with the RIVPACS approach
that became apparent, either due to its application to macrophytes, or due to its application
within the WFD.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 27
Problems associated with the application of RIVPACS to macrophytes within the WFD: 1.The expected species list is the basis of the scoring system. This implies that species will
disappear from reference conditions (the expected list) with impacts. However, with
macrophytes the occurrence of additional species is often a better indicator of impact than
the disappearance of a species.
2. The information provided by the species predicted at reference sites is low for
macrophytes since species diversity at reference sites is usually low.
3. Regular flood disturbance in rivers can uproot plants, effectively resetting any
successional community. Since it may take them some time to re-establish, identical rivers
may be at different successional stages. Aquatic macrophytes are also opportunistic, thus
the species present at a site are highly dependent on the chance arrival of propagules. This
is known as founder limitation (Yodzis 1986, Townsend 1989) and can result in
environmentally similar rivers having different macrophyte populations. A similar lack of
consistency of species within similar habitats is also evident with lake macrophytes.
4. Macrophyte abundance is important in indicating impacts, but the currently accepted
method of RIVPACS does not include an abundance component. An experimental index
(Q14) was developed in RIVPACS III, which does incorporate abundance (Wright, 2000).
5. Macrophyte surveys are different to invertebrate surveys since a whole stretch is assessed
rather than sampling representative habitats. This may introduce more noise into the
biological data, and RIVPACS does not deal well with very noisy data.
6. The low number of aquatic macrophytes at a river site often result in emergent species (or
even marginal species) being used to provide additional information to detect impacts.
Therefore the strength of the effect of the water column on the organism is more variable in
macrophyte surveys than invertebrate surveys since not all the species are submerged. This
may not be as important for lakes, where more submerged and floating leaved species are
expected.
7. RIVPACS requires at least ten predictive variables to interpolate reference conditions.
However impacts cannot be fully determined for any variable that is used in predicting the
reference conditions, so this large number of predictive variables reduces the range of
impacts that can be detected. For example, the mean substratum diameter at a site is used
for predicting the appropriate reference conditions, and therefore a siltation impact cannot be
detected.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 28
2.11 CCA-based Assessment System (CBAS) CBAS uses species optima and niche breadths derived from a CCA model to produce
biologically scaled response metrics along environmental gradients. CBAS has been
developed for rivers, but has not yet been tested on lakes.
A CCA ordination of macrophyte species is first created utilising all the environmental
variables that explain significant additional biological variance (adjusting with Bonferroni
correction (Legendre and Legendre 1998)). This, the Minimum Adequate Model (MAM),
describes the locations of species in ordination space. Multivariate species optima, and niche
breadths, along each of the environmental variable gradients are derived from this ordination.
In the ordination developed for Northern Ireland the environmental variables in the MAM
were: silt, nitrate, DO, conductivity, pH, alkalinity and slope variables. Therefore optima and
niche breadths were determined for every species along each of these environmental
gradients.
Using the same approach as the Trophic Diatom Index (TDI) (Descy 1979, Kelly 1998) the
optima and niche breadth can be used to estimate a metric value for each of the significant
environmental variables at a monitoring site:
Where:
E = Metric value (estimated value of the environmental variable, measured in species
turnover units)
ai = abundance of ith taxa at the site (square root of percentage cover)
si = optimum of the ith species (sensitivity)
vi = the indicator value for the ith species (derived from niche breadth)
Hill’s scaling is used to derive optima and niche widths so that they are scaled in standard
deviations of species turnover units with the length of the environmental variable arrow
proportional to beta-diversity (ter Braak and Verdonschot 1995).
A small niche breadth reflects a high indicator value since the species is only found at a
particular point along an environmental gradient. To generate high indictor values from low
niche breadths the indicator value is calculated as two minus the tolerance (no tolerance
values exceed two).
∑∑=
ii
iii
vavsa
E
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 29
Maximum information from the species at a site is retained since abundance is incorporated
into the metric score for a site and less reliable species (with poor indicator values) are down
weighted rather than omitted. Scores which do not use an indicator value, such as the Mean
Trophic Rank (MTR) (Holmes et al. 1999), have to omit less reliable species, even though
several unreliable species may provide more information than a single reliable species.
Metric values are generated for reference conditions from the species found at reference
sites within each river type. The reference metric values are subtracted from the monitoring
site metrics to produce measures of ecological change for each metric. The standard
deviation of the reference site metrics within a river type is used as the confidence interval
i.e. only ecological changes greater than the standard deviation are considered significant.
Alkalinity and slope metrics are not calculated since these variables are used to classify the
sites into their river types.
These metric values are to the same scale (species turnover units) and thus can be directly
compared, but they should not be directly added to produce a total value of ecological
change since there are co-correlations between the environmental gradients that form the
metrics. Although the metrics do not exist in ordination space they can be decomposed in the
same way that the environmental gradients which produce the metrics can be decomposed,
by separating their contributions into uncorrelated (orthogonal) axes. It was assumed that the
correlation of the metrics with orthogonal axes is the same as the correlations of the
associated environmental variables with the orthogonal axes (from the original CCA
ordination). The contribution of each metric to each orthogonal axis was calculated by
multiplying the metric value by the correlation coefficient between the associated
environmental variable and the orthogonal axis. Only the highest resultant value along that
axis was retained since other values represent co-correlation with the highest value. This
was repeated for all four significant ordination axes, and these four highest values were
added together to produce an approximate measure of the total ecological change.
The five metrics (silt, nitrate, DO, conductivity and pH) do not necessarily indicate a direct
alteration of this variable within the river. A change in the metric score indicates a species
change that is correlated with a change in these variables. Thus a silt metric is likely to be a
more general indicator of hydromorphological change, since siltation is often correlated with
slower flows or channel alteration.
In testing 5 unimpacted sites 80 % of metrics indicate no impact where none was identified.
In testing 20 impacted sites, 77 % of impacts were correctly identified by the metrics. CBAS
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 30
was able to detect most impacts, showed clear differentiation between impacted and
unimpacted sites, and was able to separate different impact types, particularly nitrate, silt and
pH. An objective measure of total ecological change could also be determined. The
performance is likely to be improved with a better and more comprehensive set of reference
conditions, an improved typology, and a larger data set for determining optima (all of which
are now available).
Benefits of CBAS: 1. In unconstrained ordination and classification (e.g. DCA or TWINSPAN) species are
placed together in the ordination if they co-occur at a site. However, if there is founder
limitation, species which are found in the same environmental conditions may not occur
together (since the established species will prevent the incoming species of a similar niche
establishing). However CCA uses constrained ordination, which will more accurately place
species utilising similar environment conditions closer together, even if they never co-occur.
2. Instead of predicting individual species occurrence, each metric is a species response
prediction (i.e. predicting the species optima which should occur at reference conditions).
The method here could be compared to a functional group classification (Willby et al. 2000),
but along environmental gradients instead of with species traits. As with a functional group
approach, CBAS does not rely on individual species and therefore is more robust to natural
variation. CBAS also has the additional advantage of using continuous variation instead of
discrete classes, and incorporating several gradients simultaneously.
3. The indicator value allows more information to be retained (more species retained in the
analysis), whilst down-weighting less reliable information (species) to ensure that noise is
reduced.
4. Optima estimation using univariate analysis is distorted since variables other than the
gradient being analysed influence the occurrence of the species. Within CBAS multivariate
optima are generated, minimising this effect.
5. The variables forming the MAM are those that explain the most additional significant
variance. Therefore the metrics derived are correlated with the most important impact
gradients, and correlations between the metrics are minimised.
6. CCA removes species variation due to small-scale physical heterogeneity or due to
unaccounted variables within a monitoring reach since the species responses being
measured are at the same scale as the variables used to structure the ordination. For
example, there may be two similar reaches one light and the other shaded. These sites will
have different species, but if they have the same level of silt along the surveyed reach, the
silt optima, and thus the resultant metrics, will be identical i.e. the scale of the variables used
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 31
within the ordination is the scale at which the metrics detect change. Thus noise due to both
natural temporal and natural spatial variation is reduced.
7. Optima and niche breadths, and thus the metrics, could have been determined directly for
the orthogonal ordination axes in the CCA model instead of along environmental gradients,
making assessment of total ecological change much simpler. However the position of a site
along an ordination axis does not determine whether a site is less or more impacted whereas
environmental gradients illustrate a directional change in response to an impact. This also
enables deficiencies in the reference network to be identified, since metric scores lower than
the reference river type can be detected.
8. Within RIVPACS a large number of environmental predictive variables are required and
any variable used in prediction cannot then be assessed for impacts. CBAS predicts
weighted mean optima along environmental gradients and not individual species and
therefore can produce metric predictions with fewer predictive variables.
9. Because the core of the CCA-derived metric system is based on the species responses to
an environmental gradient and not on the reference network, a change to the reference
conditions and typology does not affect the underlying model nor does it prevent the direct
comparison of subsequent metric values with historical metric values. This is particularly
important for the WFD where intercalibration may result in revision of the reference
conditions for many Member States. Hypothetical reference conditions can also be
generated since only a mean and standard deviation for the response metric need be
provided.
10. CBAS is robust to high levels of noise (due to disturbance or founder limitation) since the
CCA-derived metrics utilise combined optima, and thus the presence or absence of individual
species at a monitoring site is less important.
11. CBAS is not just a surrogate for water chemistry or hydromorphology data which can be
easily monitored in the field. At a particular monitoring site spot chemistry sampling (e.g. 12
samples) may miss sporadic nutrient releases, whereas CBAS is likely to detect these
because the species optima were obtained from 24 samples at 273 sites = 6,552 data points.
Thus the species optima represent an average response over a comprehensive data set, and
measure ecological change along the direction of an impact gradient, and not just the
specific impact.
Problems with CBAS
1. The decomposition of metrics to produce total ecological change is necessarily an
approximation. Although the decomposition is based upon correlations between the
associated environmental variables, at any site the correlation between the metrics will be
different from the correlation derived from the original data set.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 32
2. CBAS is likely to be more accurate and more sensitive to impacts if reference conditions
are generated from interpolation rather than from a fixed typology.
Further developments considered for CBAS: better data:
1. Additional data from throughout Ireland could be used in generating species
optima and niche widths.
2. Higher quality reference conditions could be used, reducing natural variation.
3. The new typology used for rivers (Dodkins et al., submitted) could be an
improvement on the previous typology used in CBAS. The lake typology should
also be optimised. These typologies will be used to ensure that reference sites
are representative of the range of river and lake types in Ecoregion 17.
better determination of species optima and niche breadths: 1. Consideration will be given to whether the variables forming the typology should
be included by default (and no other unimpactable variables) into the MAM
ordination.
2. Instead of using individual environmental variables, strongly correlated variable
types may be combined to produce more general metrics e.g. nitrate and
phosphate gradients can be combined into a ‘nutrient enrichment’ gradient, and
therefore a ‘nutrient enrichment’ metric. This should result in the metrics being
more sensitive.
3. Distance based redundancy analysis (Legendre and Anderson 1999) will be
considered as an alternative to using CCA for deriving species optima and niches.
It is a non-parametric equivalent of CCA and allows the more ecologically
appropriate Bray-Curtis similarity measure to be used instead of chi2.
4. Species will be individually assessed to determine if their optima and niche
breadth are adequately modelled by the ordination, especially for rare species
occurring less than e.g. 10 times within the data set. Species with little direct
causal link with the metric will also be rejected (e.g. many marginal species). The
ordination will be re-run with the reduced species selection list.
5. The indicator value within CBAS is currently calculated as two minus the niche
breadth, simply because no niche breadth values exceed two. However this does
not necessarily lead to optimal sensitivity and reduction in noise within the
metrics. The method of calculating indicator value should be improved, probably
by re-scaling, such that unreliable species are more suitably down-weighted and
more reliable species are given the appropriate level of importance.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 33
better estimation of ecological status: 1. Interpolation of metrics for reference conditions will be utilised instead of the fixed
typology (although the variables from the fixed typology will be used). This will
improve the accuracy and confidence in reference metric predictions and in
measurements of ecological status.
2. Consideration will be given to generating the EQR and diagnostic metrics separately,
but from the same basic method. The EQR can be derived by using the ordination
axes as metrics, thus the metrics are orthogonal and can be directly added.
2.12 Artificial Intelligence (AI) Staffordshire University has developed two types of artificial intelligence software specifically
for biological monitoring data; MIR-max (O'Connor 2002) and a Bayesian Belief Network
(Trigg and Walley 2002).
MIR-max (Mutual Information and Regression Maximisation) is a neural network which
allows species or environmental data, or a combination of both, to be classified into a preset
number of groups. A similarity measure, called Mutual Information, is used to optimise the
classification. Unlike other more commonly used similarity measures, Mutual Information is
generated from an iterative algorithm. The performance of MIR-max appears to be good, in
some cases better than TWINSPAN (Dodkins 2003) and group sizes are more even i.e.
chaining, where individual groups or species are consecutively removed from the whole, is
not a problem. The use of the method is simple and quick with a good output, and it allows
both unconstrained and constrained classification. However, due to the nature of neural
networks, the statistical support showing the effectiveness of the classification is absent.
Although it can be argued that MIR-max would produce the best classification for the data
available, it does not show if more or better quality data needs to be collected. MIR-max is
not itself a method of assessing ecological status, though it could be used in helping to
develop a suitable method.
Bayesian Belief Networks (BBNs) BBNs comprise of a network structure with interconnected nodes, with each node
representing some of the input or output data (either environmental variable values, or
species abundances). Limits to computing power prevent continuous data being used at
each node, therefore the data for each node has to be categorised.
The nodes are inter-connected to represent causal links, e.g. alkalinity would be connected
to most species to reflect the fact that alkalinity affects species distributions. These can occur
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 34
over several layers, e.g. % limestone in the catchment could be connected in the level above
alkalinity, since it has an effect on it, and alkalinity is subsequently connected to different
nodes providing species abundance. Ecological knowledge can therefore be designed into
the network.
Associated with each node is a range of probabilities of each state, e.g.:
% cover of silt 0-5 6-50 51-100
Probability of this cover 0.85 0.10 0.05
When there are two or more variables feeding into a node the joint probabilities for the node
are calculated e.g. if both silt and alkalinity feed into the Chilosyphus prediction node, we
need to know the probability of 0% abundance at 0-5% silt cover and 0-50 mg/l alkalinity, as
well as for 0% abundance, 0-5% silt and 51-100 mg/l etc (a total of 3 x 3 x 3 = 27 probability
values). Instead of keying in the probabilities by hand, the network can be ‘trained’ by
inputting field data for the abundance of species and the associated environmental variables,
enabling probabilities to be calculated.
With conventional models which use ‘exact reasoning’, the cause-effect link takes an if...then
approach, e.g. if it is a ‘tiger’ then it is a ‘cat’. Instead BBNs use plausible reasoning, e.g. if
we have a cold, there is a probability that we will sneeze. Exact reasoning is uni-directional,
i.e. ‘if it is a cat then it is a tiger’ is not true. However plausible reasoning is bi-directional, i.e.
if we sneeze there is a probability we have a cold.
The probabilities at any one node are therefore a function of the whole network, and any
changes in probabilities at one node are propagated throughout the network. Once the
network has been trained (i.e. probabilities calculated based on the training data) novel
predictions can be made. When some input data are supplied (e.g. environmental data) we
can change the probability of the states for the environmental nodes for which we have data
to 100% (since we know which state they are in). This changes the probability of all the other
nodes, and therefore we can assess the probability of certain species occurring, given the
new information. Probabilities can be calculated, given as little or as much information as we
supply. The effect of change in state within one node on the whole network can be measured
by the amount of ‘mutual information’ it provides.
Previous work with macrophytes (Dodkins 2003) showed several problems with the
application of BBN’s to macrophyte prediction. BBN’s do not work well with many terminal
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 35
nodes i.e. they cannot predict the abundance for many species. Also, the probabilities for
different abundances and different species tended to be similar so there was no clear
prediction of species abundance for a single species.
Theoretically BBN’s are extremely powerful, being model free (i.e. no assumptions about
species distribution curves), and by enabling the incorporation of ecological knowledge
through the relationship between nodes. The poor results in initial testing by (Dodkins 2003)
may have been due to the design of the nodes and the focus on species prediction. Instead a
different, untested, BBN method is proposed:
New BBN Method We wish to produce a five-band ecological status classification for rivers and lakes using
macrophyte cover data. Ecological status classification is dependent on the river or lake type,
and thus the variables that form the typology. Therefore the input to the model is the species
data, plus the allocation of sites to river or lakes types within a typology. Additional,
unimpactable environmental variables could also be collected to improve prediction
accuracy. The output is simply the status bands.
To produce the model, status bands have to be initially derived at the sites at which we have
data. This has an enormous advantage since using visual inspection as well as chemical and
hydromorphological data we can determine using expert judgement the status of the site (for
that river or lake type). This avoids status categories being assigned in the office where the
whole catchment perspective and understanding of the site cannot easily be determined.
This BBN calibration method also acknowledges and directly deals with the problem of
ecological status being subjective.
As with MIR-max, the statistical support for the output of the BBN is not available. However it
may be possible to use conventional statistical methods could be used to determine
confidence in the output of the data. The distribution of the probabilities for the status class
can also help to provide confidence information. This is especially important if there are high
and bad quality states that have similar species (e.g. very low species numbers at low
nutrient or impacted sites) since the distribution of probabilities will show this.
There are three main problems with developing this BBN. Firstly, the input data all has to be
categorical and not continuous, and optimising the categories could be difficult. The second
problem is that this method has little diagnostic capability, and further investigation (in the
field or of the data) may be required to determine the cause of the impact. The BBN may
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 36
allow potential identification of problems by removing individual chemical or
hydromorphological input data and determining the deviation between the predicted data
point and the actual data (illustrating that the chemical or hydromorphological data collected
does not fully represent the strength of ecological impact). Thirdly, BBNs are completely
dependent on calibration with the data supplied. Therefore the BBN requires data which is
sufficient in quantity and quality.
David Trigg at Staffordshire University (pers. comm) suggests that if this approach is taken,
firstly, good quality data are required. If the model performs with large numbers of parent
nodes (nodes towards or at the data input level), it is better to structure the model so that
there are more levels of nodes (not simply input and output, but some of the data exists in
interceding layers), preventing excessive geometric growth (i.e. reducing the computation
power required).
3.0 Discussion All the available methods examine gradients underlying the macrophyte ecology, whether
they be modelled (CBAS), defined through expert judgement (MTR) or through the
amalgamation of a selection of different gradients (multi-metrics). All these gradients are an
attempt to measure ecological change, however that is defined. Therefore the major criteria
for comparison of these methods is:
Does the method measure ecological change due to impacts effectively?
To achieve this, the method must fulfil several targets:
1. Ecological change must be scaled correctly i.e. a change from one status band to another
is equivalent in terms of ecological change, regardless of the river or lake type.
2. There can be no inflexion point within the score when plotted against ecological change
e.g. the EQR for good status should not be the same as the EQR for bad status for any site.
3. The score must work for all lake or river types. For example in a before/after assessment
using RIVPACS III, a siltation impact was not detected in upland sites because the data used
to produce the model did not include upland sites that had siltation (Armitage 2000).
4. The method must detect ecological change due to impacts, whilst ignoring changes due to
natural variation.
5. Ecological change (such as species loss or functional change) and not the level of impact
(e.g. TP concentration) should be measured.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 37
The methods described in this review can be broken down into three basic classes of
method:
1. Expert-based metrics and multi-metrics (e.g. Free’s lake assessment method, MTR,
USEPA multimetrics, SEQC, STAR/AQUEM, ECOFRAME, MPhI)
2. Reference and indicator species ratios (Willby’s and Schaumberg’s methods)
3. Optima modelling (e.g. CBAS)
CBAS gives an objective measure of ecological change, but multi-metrics can also examine
many functional aspects of the environment. However metrics are usually based on values
related to species compositional change. These are created through expert selection of
functional groups or ranking scores. Statistical modelling (e.g. CBAS) can be used to create
species compositional change scores which will be related to a gradient in a more accurate
and objective way than expert-based metrics.
Whichever method is used there is likely to be one main gradient which can be best detected
by ecological survey. Subsequent gradients are likely to be more poorly detected because
the first impact gradient will have the main effect on the species and thus species which
respond more to the first gradient will distort the interpretation of the secondary gradient. This
is dealt with within metrics by using a reliable functional group or removing species which are
unreliable along the selected gradient. However a limit to the number of gradients which can
be detected is reached, regardless of the method used, since eventually almost all the
species are more strongly influenced by previous gradients. CBAS down-weights less
reliable species by utilising the niche breadth along the gradient, preserving as much
information as possible for each gradient whilst reducing noise.
Metrics are unlikely to select the optimal combination of gradients, whereas CBAS
maximises the separation of gradients (if suitable environmental variables are contained
within the model). Therefore, if sufficient environmental data are available, CBAS is likely to
outperform any other compositional metric. The large numbers of metrics that can be
available in a multi-metric system is only an apparent benefit, since once the maximum
number of gradients has been utilised, further gradients will just be correlates of previous
gradients. An advantage of multi-metrics can be the incorporation of functional metrics, such
as zone of colonisation. Rather than combine functional metrics with subjective
compositional metrics it would be more useful to combine them with more accurately
modelled compositional metrics e.g. those from CBAS.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 38
Expert scores such as MTR, have the same limitations as other metrics. MTR was designed
to be used on physically similar sites; usually before/after an impact at the same site or using
a site just upstream of the impact as the control, and not as a measure to be used within a
typology. Figure 3.1 shows a plot of MTR against total phosphorous (TP) and log TP, taken
from the Environment Agency review of the MTR system in both England and Wales and
Northern Ireland using (Dawson et al. 1999). There is a highly significant correlation (P =
0.001) for England and Wales. However, the correlation in Northern Ireland is poor (r = 0.253
for log TP), and has poor significance (P = 0.10).
It is known that MTR performs best at TP concentrations less than 0.5 mg/l , but that it
performs poorly above 1 mg/l (Dawson et al. 1999), and this can also be seen from Figure
3.1 a and 3.1 b. Within these figures the range of MTR values have been divided into five
even bands, representing ecological status classes to determine whether sites in one status
class are within a defined range of MTR scores. Within England and Wales MTR seems
capable of distinguishing what could be considered high and good quality classes, but none
of the other quality classes can be distinguished. In Northern Ireland the relationship of MTR
with TP is even poorer (Figure 3.1 c and 3.1 d) and the quality classes do not represent any
clear change in TP. The addition of a typology is unlikely to add sufficient precision such that
high or good status sites may not be incorrectly designated as moderate status or worse.
This can be directly compared with the metrics produced from CBAS for Northern Ireland
(Figure 3.2). The correlations of all five of the CBAS metrics are better than MTR, and with
the addition of a typology the EQRs are expected to be adequate to differentiate between
status classes for at least the silt and nitrate metrics.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 39
Figure 3.1 Relationship between MTR and phosphate concentration (IFE and EA data
for England and Wales, IRTU data for N. Ireland matched to phosphate correlations within
1km). (a) MTR against TP in England and Wales (b) MTR against log TP in England and
Wales (c) MTR against TP in N. Ireland (d) MTR against log TP in N. Ireland. Ecological
quality classes indicated on the diagram are (h) high, (g) good, (m) moderate, (p) poor, (b)
bad. (***) P = 0.001, (**) P = 0.01, (*) P = 0.1. NB. TP Limit of detection in N. Ireland data is
0.05 mg/l. From (Dawson et al. 1999).
h
g
m
p
b
h
g
m
p
b
h
g
m
p
b
h
g
m
p
b
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 40
Figure 3.2 Relationship between CBAS metrics and their respective environmental gradients
Internal validation of CBAS using 273 river sites in Northern Ireland. Environmental values
(x-axis) are plotted against position along this environmental gradient predicted by the
macrophyte species that occur at the site (y-axis). DO is mean dissolved oxygen (%);
NITRATE is mean nitrate concentration; PH is mean pH; SILT is % silt cover in channel; and
COND is mean conductivity. All means are over two years prior to macrophyte sampling.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 41
It is argued that MTR scores represent trophic status, being much more than a measure of
TP. However, if expert-based scores are not defined, they are untestable, whereas if expert
scores are clearly defined, either ecologically or by the variables to which they respond,
these variables can be combined within ordination into a single gradient, from which CBAS
can produce a more accurate measure of ecological change. A problem with CBAS is that it
requires a large amount of high quality macrophyte data and physico-chemical data.
However such data will be available for Ecoregion 17.
Willby and Schaumberg both use reference and indicator species ratios to determine the
EQR. Perceived impact is ranked for each river or lake type, with the first method using an
expert-based metric and in the second method using expert-based ranking of impact at sites.
An advantage of this approach is that impact and reference species lists are produced for
each lake or river type, which is likely to increase the sensitivity of the methods. However the
sensitivity is later reduced by using an indicator/impact ratio which replaces what could be a
gradient of response for each species, to a binary response (though this is likely to make the
methods more robust to natural variation).
Within Willby’s method the application of ordination methods can be criticised since the initial
DCA ordination is presumed to be correlated with trophic status, whereas it may be
correlated with alkalinity. Also, the use of alkalinity as a co-variate within the CCA may
remove an important component of the trophic gradient and the use of MTR may not be
appropriate as a basis of the scores in Ireland. Although other impact metrics can be
combined with the MTR based scores (such as a hydromorphological index), these will not
be the least correlated gradients for these two attributes, and thus the response to one metric
may unduly interfere with the response of the other metric. Schaumberg’s method and
LEAFPACS both determine impact by a subjective ranking of species along a perceived
impact gradient, which is not verifiable. Within Schaumberg’s method it is unlikely that any
more than one gradient is really being determined, though this depends on the impacted river
and lake data set.
It was previously noted that metrics often have high rates of type II error, due to natural
variation and the variation of the metric response in different lake or river types. The
ECOFRAME method deals with the both these problems. Firstly it filters out natural variation
by using a probabilistic method in which only 80% of metrics need to be of a certain quality
class to enable a site to be designated as belonging to that class. Secondly, because the
status of each metric is arrived at independently, prior to combination, there is no problem
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 42
with combining non-linear metrics that have incompatible scales. See Figure 3.3 for an
example.
Figure 3.3 The benefit of assigning a quality class prior to metric combination a. Assigning a quality class after metric combination: the same quality classes are used
for each metric regardless of the different metric response along an impact gradient.
Therefore ecological quality class boundaries may not relate to the level of ecological
change. The alternative is to use only metrics with a linear response.
b. Assigning a quality class prior to metric combination: Different metric response
curves can easily be matched so the level of ecological change is directly comparable
between metrics. Non-linear responses can easily be combined.
Artificial Intelligence methods do not fit easily into any of the three assessment method
categories described above. There seems to be great potential with AI, but a suitable method
has not yet been created, and time may be inadequate to create and test a method. Being
‘model-free’ may not necessarily be a benefit with AI. For example, a Gaussian response
curve (e.g. in CCA) is likely to reduce the noise in a biological data set whereas AI will
include the noise in the data interpretation. In addition, there is a problem with models that
use probabilities and correlations for prediction rather than mechanistic ecological knowledge
Ecological response
metric value metric value
h m g b p h m g b p
metric value metric value
Ecological response
h m g b p h m g b p
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 43
(direct understanding of cause-effect). i.e. Some species may be correlated with water
quality (e.g. Agrostis stolonifera) but only have a causal association with the landscape (e.g.
farming) rather than with the water quality. Over a large data set, where farming is often
correlated with reduced water quality, these species will be seen to be good indicators of
enrichment, which in fact they are not. This may have the effect of suggesting no
improvement in ecological status, even when highly effective land-management schemes are
undertaken which reduce pollution. Therefore modelling methods (including CBAS) have to
be carefully designed with a comprehensive knowledge of the underlying assumptions the
model is making, and of cause-effect linkages.
Metrics have been successfully used by the USEPA, however the requirements of the WFD
are different since legal decisions will be made on a final aggregated score. Thus the method
of metric combination should be carefully considered so that, as far as can be perceived, it
accurately represents a measure of ecological change. A well-designed typology is also
fundamental to the success of any ecological assessment method since it will help to reduce
noise caused by natural variation. Unfortunately within the WFD submission of typologies
was required prior to development of the assessment methods, whereas a typology would
ideally be designed subsequently, to minimise the natural variation occurring within the
particular ecological assessment method. Table 3.1 lists the ecological assessment methods
reviewed and summarises their main benefits and drawbacks.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 44
Table 3.1 Benefits and drawbacks of the different ecological assessment methods
Ecological Assessment Method
Benefits Drawbacks
Expert Scores (MTR
etc)
Simple Unfalsifiable. Gradients better
estimated through ordination.
USEPA
bioassessment
Simple High type I and II error. USEPA
does not utilise a final metric
combination score for decision
making. Combining error prone
metrics for the WFD is likely to
produce low confidence in status
classes.
STAR/AQUEM Simple Metrics not combined
appropriately; ecological status
dependent on number and choice
of metrics. Prone to same
problems as USEPA
bioassessment.
SEQC Good method of metric
combination.
Underlying expert score (TRS) is
poor since it is highly correlated
with alkalinity.
ECOFRAME Excellent method of
combining diverse
metric scores. No
major reasons for
rejection if the selection
of metrics is
appropriate.
Compositional metrics likely to be
better designed by ordination
methods.
Free’s multimetric
index
Functional metric may
be useful.
Compositional metrics likely to be
better designed by ordination
methods. Rejection of non-linear
metrics unnecessary if
ECOFRAME method of
combination used.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 45
Macrophytoindication A functional metric. An assessment of total plant cover
and therefore does not represent
overall ecological status. Likely to
have a poor sensitivity.
Schaumburg’s
Vegetation Tables
Simple and follows
WFD ethos.
Reference/impact
indicator ratio likely to
reduce noise.
An expert-based method in which
the underlying gradient is unlikely
to be better than that derived
through ordination. May not detect
more than one underlying impact
gradient. Highly dependent on the
selection of reference and impact
lakes. Reference/impact indicator
ratio likely to reduce sensitivity.
LEAFPACS Reference/impact
indicator ratio likely to
reduce noise.
An expert-based method in which
the underlying gradient is unlikely
to be better than that derived
through ordination. Inappropriate
use of ordination methods.
RIVPACS Objective measure of
ecological status.
Unworkable; unable to predict
large enough species lists for
reference conditions.
Reference/impact indicator ratio
likely to reduce sensitivity.
CBAS Models underlying
gradients well. Has an
objective overall
measure of ecological
quality. Tested
successfully in rivers
but not lakes.
Care required to ensure that there
is a causal link between the
species and the underlying
gradient forming the metrics.
Artificial Intelligence No method yet shown to be
effective. Insufficient time for
development. ‘Model free’ so may
not reduce noise and may have
incorrect and unidentifiable
underlying assumptions.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 46
4.0 Conclusions 1. Methods based on MTR and TRS metrics show poor performance within Ecoregion
17. MTR has not been tested for comparability within a varied physical environment,
and is expected to be ineffective at distinguishing quality classes.
2. Any simple species composition metrics are not expected to perform as well as
CBAS.
3. Ecological assessment methods required for the WFD and therefore the prime
objective is to measure change in overall ecological status, not to produce diagnostic
measures. CBAS is likely to determine underlying gradients in a more effective
manner than any other methods discussed, and produces an objective measure of
ecological status. This does not preclude combination of CBAS with other metrics,
which may represent functional aspects of the ecology better, or the use of diagnostic
metrics as separate from the ecological status assessment required for the WFD.
4. If multi-metrics are to be adopted it is suggested that metric combination should use
the approach of ECOFRAME i.e. separate status assessment for each metric, and
accepting failure in a proportion of the metrics. Time may be too limited for
developing and testing AI approaches.
5.0 Recommendations It is recommended that CBAS undergoes development for use in rivers, and that it is tested
for suitability in lakes.
Consultation on the deficiencies of compositional metrics and Willby’s method should be
sought to determine if these methods could be rejected before time-consuming testing takes
place. AI developments with phytobenthos ecological assessment methods for the WFD
should be followed to determine if an AI approach could be transferred for use with
macrophytes.
It is recommended that LEAFPACS and the CBAS based assessment system be tested on
the same data set to compare their performance. The Free Index and LEAFPACS are
currently being used in intercalibration.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 47
RIVERS
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 48
Exploring changes to CBAS 31st May 2005
In the previous section reviewing methods of assessing ecological status in rivers and lakes
with macrophytes potential improvements to the CBAS method (Dodkins et al. 2005a) were
advanced. This technical report summaries the examination of potential developments within
CBAS that are possible prior to submission of the method in 2006. Five main potential
improvements were identified:
1. The use of distance based redundancy analysis rather than Canonical
Correspondence Analysis (CCA) for developing the CBAS model.
2. The use of absolute rather than relative abundance in a CCA model
3. Examination of the benefits of CCA over DCA.
4. Combination of several environmental variables to form one environmental gradient
5. The improvement of reference site prediction accuracy and confidence by using
interpolated reference conditions rather than a fixed typology.
1. Distance Based Redundancy Analysis (db-RDA) Introduction
Bray-Curtis similarity measures are often used with biological data since it is effective with
data which contains many zeros (Legendre and Legendre, 1998). However CCA, which
produces the model for CBAS utilises chi-squared distance. Distance based redundancy
analysis (db-RDA) is a method in which any similarity measure, including metric and semi-
metric measures (e.g. Bray-Curtis), can be used within a constrained ordination.
(Legendre and Anderson 1999) detail the procedure for distance based redundancy analysis
(db-RDA), and an example is also provided on page 308 of the CANOCO 4.5 manual (ter
Braak and Šmilauer 2002). Canonical Analysis of Principal Coordinates (CAP) (Anderson
and Willis 2003) is equivalent to db-RDA, except that it utilises Canonical Correlation
Analysis (CCorA) instead of RDA. CCorA is not incorporated into CANOCO software since
(ter Braak and Šmilauer 2002) consider the inclusion of RDA to make CCorA superfluous.
RDA can analyse any number of species, whereas CCorA can only analyse a number of
species equal or less to the number of samples minus the number of environmental
variables. However (Anderson and Willis 2003) notes that in CCorA the analysis takes into
the account the correlation structure among both the environmental and species abundance
variables whereas RDA only takes into account the correlation structure among
environmental variables.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 49
Method
The 273 site EHS data, used to produce the original CBAS model (Dodkins et al. 2005a),
was analysed using db-RDA with a Bray-Curtis similarity measure.
Conducting a db-RDA
Principal co-ordinates analysis (equivalent to metric multi-dimensional scaling) is used to
produce a (Bray-Curtis) similarity matrix for the species and for which latent underlying
gradients are determined as axes. Principal co-ordinate analysis (PCO) can be achieved
most easily within separate software which is included within CANOCO 4.5, or alternatively
through using PCA (ter Braak and Šmilauer 2002) p.64. The axes co-ordinates, directly from
the PCO data file output, are then used as species within RDA, and the normal species data
are used as supplementary data. To prevent over-parameterisation only the first 6 PCO axes
were used (Anderson and Willis 2003). When an RDA ordination diagram is displayed the
environmental variables and ordination axes are determined in relation to the principal co-
ordinates, and thus are configured by the Bray-Curtis similarities. To display the species as
centroids of their occurrence, the supplementary data must be selected as nominal variables
(Project/Nominal variables).
db-RDA does not provide eigen values, and being a fundamentally different procedure to
CCA is difficult to compare. Thus, as well as conducting db-RDA with Bray-Curtis distances,
and a CCA, a db-RDA was also conducted with chi-squared distances. Manual forward
selection was conducted on the models (accepting only variables which explained significant
additional variance) to determine the models. Finally, ordination diagrams of CCA and Bray-
Curtis db-RDA were produced to examine differences between the species locations.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 50
Results
Species-environment correlations (equivalent to r-squared values in linear analyses) show
the chi-squared measure to be better along the first axis than the Bray-Curtis measure
(Tables 1 and 2), although this is no consistent. The chi-squared db-RDA also explains more
overall variance (67.5 % compared to 57.5 %). However the Bray-Curtis db-RDA explains
more species variance in the first few axes (cumulative percentage of species). Although the
species-environment relation (which indicates how well the constrained axes explain the
species data) is better for the Bray-Curtis db-RDA than the chi-squared db-RDA, the CCA is
better than either.
Forward-selection of environmental variables with the Bray-Curtis db-RDA produced a model
with slope, conductivity (though alkalinity could be used), nitrate, substrate and pH; similar to
the CCA based CBAS, although the order of importance is different.
The ordination diagrams from Bray-Curtis db-RDA and CCA produce quite different
ordinations (Figure 1 and Figure 2).
Table 1. db-RDA using Bray-Curtis similarity. All PCO axes were extracted and the model
based on significant variables of: slope, conductivity, nitrate, subs, pH. NB notice eigen-
values are not given.
Axes 1 2 3 4
Total
variance
Eigenvalues : - - - -
Species-environment correlations : 0.781 0.828 0.827 0.855
Cumulative percentage variance
of species data: 3.4 5.2 6.7 7.7
of species-environment relation: 8.7 17.1 23.9 28.9
Sum of all unconstrained
eigenvalues 1
Sum of all canonical eigenvalues 0.575
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 51
Table 2. db-RDA using Chi-squared similarity. All PCO axes were extracted and the model is
based on silt, nitrate, pH, slope, conductivity, alkalinity and dissolved oxygen. NB notice
eigen-values are not given.
Axes 1 2 3 4
Total
variance
Eigenvalues : - - - -
Species-environment correlations : 0.819 0.739 0.814 0.733
Cumulative percentage variance
of species data: 2.1 3.5 4.3 4.9
of species-environment relation: 4.0 7.2 10.4 12.3
Sum of all unconstrained
eigenvalues 1
Sum of all canonical eigenvalues 0.675
Table 3. CCA summary using CBAS model based on silt, nitrate, pH, slope, conductivity,
alkalinity and dissolved oxygen.
Axes 1 2 3 4
Total
Inertia
Eigenvalues : 0.421 0.254 0.183 0.135 13.160
Species-environment correlations : 0.854 0.757 0.715 0.632
Cumulative percentage variance
of species data: 3.2 5.1 6.5 7.5
of species-environment relation: 34.2 54.8 69.7 80.7
Sum of all unconstrained
eigenvalues 13.160
Sum of all canonical eigenvalues 1.230
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 52
Figure 1. db-RDA with Bray-Curtis similarity measure and only first 6 PCO axes.
Figure 2. CCA (chi-squared distances) Species Conditional Biplot from the original CBAS
method.
-1.0 +1.0
SILT
SLOPE
COND
DO
RALK
NITRATE
PH
nuph lut
raco spp
hygr spp
alis pla
lemn min
brac riv
call sppchil pol
pota perpota nat
fort squ
scho
spar ererhyn rip
cono con
peta hyb
spon
fila gre
glyc flu
cinc fon apiu nod
spar eme
sola dul
font ant
ambl rip
hild riv
equi flu
ment aqu
lema
myri spi
phal aruoena cro
ranu pen
clad aeg
-1.0 1.0
-0.6
1.0
Ax1Ax2
Ax3
Ax4
Ax5
Ax6
SLOPE
SUBS
PH
COND
NITRATE
alis pla
ambl flu
ambl r ipapiu nod
azol filbatr
beru ere
brac plu
brac r iv
buto umb
call cus
call ham
call obt
call spp
calt pal
care spp
chil pol
cinc fon
clad aeg
cono co n
diat algdich pel
eleo pal
elod can
elod nut
epil hir
equi arv
equi flu
equi pal
fila g re
fili ulmfiss spp
font ant
fort squ
gali pal
glyc flu
glyc max
hera man
hild r iv
hygr spp
hyoc arm
iris pse
junc artjunc bul
junc eff
lema
lemn gib
lemn min
lemn pol
lemn tr ilitt uni lunu cru
lyth sal
marc pol
mars ema
me nt aqu
meny tri
mimu gu t
mniu pu n
myos sco
myri altmyri spi
nard com
nuph lut
oena cro
oena flu
orth r iv
pe ll endpell epi
pe ta hyb
phal aru
phra aus
plag rosplag und
polg hyd
poly amppoly compota cri pota gra
pota luc
pota nat
po ta pec
pota per
pota sa l
raco spp
ranu f la
ranu p en
rhyn r iprhyt squ
rori am p
rori nasrume hyd
sagi pro
sagi sag
scap und
sch o
scro aqushis sppsium lat
sola dul
spar eme
spar ere
spon
stac pal
tham alo
vauc
vero ana
vero bec
verr spp
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 53
Discussion
Chi-squared db-RDA and Bray-Curtis db-RDA appear to be examining slightly different
aspects of the species data. (Anderson and Willis 2003) p.520 state that “...chi-squared
distances emphasise differences in composition of assemblages, whereas Bray-Curtis
dissimilarities tend to emphasise differences in relative abundance”. In a founder limited
ecological system (Yodzis 1986, Townsend 1989) or with high natural variation, Bray-Curtis
may perform worse. The value of using db-RDA is not clear; the ordination quality is not an
improvement and the species optima do not to be an improvement (although further
investigation may be needed).
Recommendations
There could be further investigation into the data transformation (possibly using fourth-root
square abundance), the similarity measure used, and different ordination or other methods
for obtaining optima. The best methods are likely to depend on the nature of the gradients
which are assessed, and which characteristics of the community are best for determining
different impacts (which may be different for different impacts). Within CBAS, reconstruction
of the impact is not achieved through ordination, but through the weighted averaging method
used within the TDI. Therefore the optimisation of impact predictions (and thus ordination
method choice and data transformation) is highly dependent on how the weighted averaging
equation utilises the optima and niche breadths. There is insufficient time within 2006 to fully
investigate these issues. CCA performs well in determining impact gradients, optima, and
niche breadths, and there are no striking advantages of db-RDA. CCA should be retained
within CBAS for the first submission of the method in December 2005.
2. Use of absolute rather than relative abundance Introduction
CCA calculates similarity between sites from relative abundances of species at a site (ter
Braak and Šmilauer 2002) i.e. the amount of each species as a proportion of the total
percentage macrophyte cover (which is usually less than 100%). Potentially this could result
in a loss of information since there may be an ecological difference between for example, if
two sites each have the same two species at the site (and no other species), but at one site
each species has 0.1% cover, and at the other site each species has 10 % cover, there may
be an ecological difference between the sites. However channel width (or lake size) may also
become more important in the ordination, since absolute percentage abundance will be more
dependent on available colonisable habitat.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 54
Method
Absolute percentage abundances were determined by adding percentage 'bare area' as a
species within the CCA analysis. This was calculated as 100 minus the sum of the species
found at a site, checking that none exceeded 100%. The 273 site 1998 EHS Northern Ireland
river data set was used within a CCA analysis with both absolute and relative abundance
data. A Minimum Adequate Model (MAM) was constructed for both models by using step-
wise forward selection within CANOCO to determine which environmental variables
explained additional significant variance in the species data (adjusting significance with
Bonferroni correction). Ordinations were then conducted on the data.
Results
Eigen-values were found to be much lower with absolute abundances (Table 4). The
minimum adequate model (MAM) for the absolute abundance model was similar to the
relative abundance MAM (% silt, nitrate, pH, slope, conductivity, alkalinity, dissolved oxygen),
although slope was omitted and temperature, bank pebbles and width were now significant
within the model. Ordinations using the same set of variables for the relative abundance
MAM (silt, nitrate, conductivity, dissolved oxygen, alkalinity, pH and slope) the ordinations
were nearly identical (and are therefore not included).
Table 4. Eigen-values produced with CCA of 273 Site N. Ireland data with absolute and
relative abundances.
1st
axis
2nd
axis
3rd
axis
4th
axis
absolute
abundance
0.242 0.152 0.121 0.098
relative
abundance
0.421 0.254 0.183 0.135
Discussion
Absolute abundance ordination enables the examination of slightly different characteristics of
the macrophyte community. Suprisingly, the important slope variable is omitted from model,
although other physical variables, such as stream width and temperature, gain in importance.
This may be due to absolute abundance being affected by available habitat. The ‘bare area’
species tended to be of far higher abundance than any other individual species, thus adding
little difference to the relative abundance ordination except noise, and thus explaining the
similarity of the two ordinations.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 55
The ordination using absolute abundance was of much worse quality (lower eigen values),
produced a less realistic model, and although optima for species were similar, is likely to be
less accurate than the relative abundance ordination. It is therefore recommended that CBAS
continues to use relative abundance within the model construction.
Within this study the absolute abundances were still relative between rivers i.e. a percentage
scale was used. An alternative would be to assess absolute abundance by considering the
habitat cover provided by the species in m2. This could be achieved within the data if the total
survey area for each river is known. However it was felt that, in light of the investigation
above, that this would only add extra information in the form of a weighting relative to river
size, which was not considered useful.
3. Investigation into the benefits of CCA over DCA Introduction
Within (Dodkins et al. 2005a) it was suggested that the CCA model would perform better
than a DCA model where there was high natural variation or where founder limitation occurs
(Yodzis 1986, Townsend 1989, Dodkins et al. 2005a). It was thought that, with two species
that have the same niche, but do not co-exist due to competition, a DCA ordination would
separate the species since DCA utilises co-occurrence to produce an ordination. CCA is
constrained by environment gradients, and thus would be more likely to place species which
exist within the same niche together, even if they never co-exist. This effect was observed in
a previous study with artificial data in which two out of ten species were constructed to have
identical optima, but did not co-exist (Dodkins, unpublished). However the number of species
in the analysis was considered to be too low to give a true representation of real effects.
Method
The 1998 EHS 273 river site data set was 'dosed' the artificial macrophyte data called spp x
and spp y. Spp x and spp y were constructed such that they had the same optima along a
nitrate gradient but never co-occurred (an example of extreme founder limitation). The
hypothesis was that the CCA Minimum Adequate Model used within CBAS would correctly
place species x and y together whereas DCA would (incorrectly) separate them.
Results
CCA correctly located species x and y at the same position along the nitrate gradient (Figure
3), despite the species never co-occurring. However the separation of spp x and y was large
perpendicular to the nitrate gradient, such that the separation was greater than that in DCA
(Figure 4).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 56
Figure 3. CCA of 273 site EHS river macrophyte data dosed with artificial species x and y,
which have the same optima, but never co-occur. All 111 species are not shown for clarity
(weight > 8%). Scales within this Figure and Figure 4 are not directly comparable, however
the distance between spp x and y can be considered relative to the distance between other
species.
Figure 4. DCA of 273 site EHS river macrophyte data dosed with artificial species x and y,
which have the same optima, but never co-occur. All 111 species are not shown for clarity
(weight > 8%). Scales within this Figure and Figure 3 are not directly comparable, however
the distance between spp x and y can be considered relative to the distance between other
species. Only an extract of the DCA figure has been drawn.
-1.0 1.0
-1.0
0.6
SPP X
SPP Y
SLOPE
SILT
ALK
PH
COND
DO
NITRATE
3.0
4.0
5.0
-0.1 0.7
1.0
8.0
SPP X
SPP Y
ambl rip
chi l pol
nuph lut
rhyn rip
s par ere
SLOPE
SILT
RALK
PH
COND
DO
NIT RAT E
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 57
Discussion
The large separation of the dosed species within CCA may be due to the differences in other
environmental variables at the sites at which species x or species y occurred (since they
never co-occurred, and the optima was only considered along the nitrate gradient). Thus, if
the species had been designed to exist in the same niche with regard to other variables this
separation within CCA is unlikely to have occurred. Co-occurrence of each of these species
with other species within the contingency table was probably the reason why DCA correctly
placed the dosed species close together. However, DCA incorrectly separated these species
along the superimposed nitrate gradient.
Thus DCA is able to deal with founder limitation (though not necessarily noise). However
CCA is better at deriving optima along environmental gradients, and would be expected to
perform better if the dosed species had similar niches along other gradients (which is likely
with natural founder limited species rather than artificial data). CCA tends to remove the
effects of variation due to variables that are not included in the model (usually noise). This is
ideal for modelling but is not good for determining whether all the relevant variables have
been included within the model i.e. large differences between CCA and DCA suggest that
unmeasured variables (rather than biological noise) is the cause of the difference.
CCA was determined to be much better for species-environment modelling and optima
derivation, and thus the advantage of CCA over DCA within CBAS are supported, although
the reasons for this advantage are slightly different to those first proposed.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 58
4. Combination of several variables to form one environmental gradient Introduction
Within CBAS several related environmental gradients (e.g. nitrate and phosphate) could be
combined into a single impact (or natural) gradient, thus providing a more general
representation of e.g. hydromorphology or eutrophication.
Discussion
The two main benefits of combining environmental gradients are i. in explaining more
variance in the species data (although this may not always be the case), and ii. to produce
more general metrics e.g. of hydromorphology and nutrient enrichment. The main drawbacks
are that i. it may reduce the diagnostic ability of the metrics, since the metrics are not distinct,
ii. it may reduce the ability to distinguish the impact from aspects of the natural structure, and
iii. more noise may be introduced (even though it is the same latent gradient being drawn out
in each case).
Other physical and chemical gradients will need to be included in the model if combined
environmental gradients are to be used (e.g. phosphate, % boulders in channel, width/depth
ratio etc). Within CCA this increase in environmental gradients reduces the level to which the
ordination axes are constrained, which is likely to be detrimental since constrained axes
reduce natural variation.
Ideally an impact gradient would be created which is orthogonal to the underlying natural
gradients, but this is never likely to be the case since impacts tend to be correlated with the
strong underlying natural gradients. Although Nigel Willby (unpublished) has attempted to
remove the correlation between natural and impact gradients, though removing the
correlation also reduces the impact signal. The stronger the correlation between impact and
natural gradients (e.g. alkalinity and trophic status) the more the removal of co-correlation
affects the real impact signal. There is no appropriate method of separating natural and
impact gradients other than by utilising reference sites (see ecological assessment methods
review).
Another alternative is to use gradients that are not directly related to water chemistry or
physical structure measurements. For example, if MTR (Holmes et al. 1999) or indeed any
other gradient is considered a better score of eutrophication or impacts, the gradient could be
used within a CBAS approach. In this way, it could be determined whether the gradient
explains significant variance in addition to the natural underlying gradients (e.g. alkalinity). If
so the optima of species along the gradient could be scaled more accurately. The correlation
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 59
with other impact or natural environmental gradients and the variance explained within the
species data could also be compared.
Conclusion
Combining impact gradients to develop metrics may be useful, though noise is likely to be
introduced, the model may be less robust with high natural variation, and precision in
determining impacts may be affected. Potentially there could be improvements to the
gradients used in the CBAS model, although direct combination may be too simplistic. Non-
environmental gradients (e.g. MTR) also have scope for inclusion within a CBAS type model.
However single environmental gradients will be retained within CBAS.
All metrics which utilise species composition relate the species composition to an underlying
gradient, whether this is explicit (as in nitrate gradients in CBAS) or not (‘eutrophication’
gradient in MTR). Once the gradient has been defined, even if it is not defined explicitly (e.g.
MTR or vegetation tables (Schaumburg et al., 2005)), the CBAS method is likely to
outperform the designed metric in i. correlating species to the gradient and ii. separating the
correlation of the metric with natural underlying gradients, and iii. scaling the species optima
along the gradient.
5. Reference site interpolation Introduction
Within a discrete river typology there is a sudden jump in the state of the reference condition
between one river type and the next. Although there can be ecological patchiness within
rivers and lakes, this patchiness is more likely to coincide with discrete features such as
incoming tributaries, than with the variables used to form the river or lake typologies (based
on slope and alkalinity, or alkalinity and depth). At a larger scale the river and lake types will
form a continuum (Vannote et al. 1980, Poole 2002).
Reference site interpolation will improve predictions by producing reference conditions more
specific to the exact slope, alkalinity or depth values. It will also prevent a sudden change in
reference conditions when moving between discrete river or lake types e.g. from 100mg/l to
101 mg/l CaCO3. Interpolation is also expected to reduce error values in predictions, since
the errors can be based on the nearest neighbours.
Method
The 273 river site EHS data set, along with 32 reference sites from Northern Ireland (Dodkins
2003, Dodkins et al. 2005a), was utilised to test reference site interpolation.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 60
Slope and alkalinity now form the reference typology for Ecoregion 17. Fortunately these are
also the only unimpactable variables which structure the previous CBAS model. Thus slope
and alkalinity were chosen as the two variables on which reference conditions were to be
interpolated.
Multiple linear regression was first considered for interpolating reference conditions from the
reference sites. However, this was rejected since there is no reason why the reference
condition metrics from CBAS should follow a linear (or even unimodal) response with
changes in silt and alkalinity. RIVPACS utilised nearest neighbours of pre-classified groups
(Wright et al. 1984) to interpolate reference conditions, however a conceptually simpler, and
probably more accurate method is krigging. Krigging determines a value from the
neighbouring sites; weighted by the proximity of these sites. However it is mathematically
complex and available software only allows krigging in two orthogonal dimensions.
Fortunately there are only two typology variables, and although slope and alkalinity are not
orthogonal, the development of the CBAS model ensures that these gradients are two of the
least correlated underlying gradients within the species data. The distances between the
axes will be distorted by forcing correlated variables into x/y coordinates, but this distortion is
regular along both krigging axes, such that simple transformation of the data would produce
a similar result. A slight variation in the accuracy of the nearest neighbour distance may be
expected, although the effect may not even be evident.
Method
Data transformation is not as important within krigging as it would be within multiple linear
regression since krigging specifications are distance based. The geostatistical wizard in
ARCMAP (ESRI 2002) was used to conduct krigging and produce contour maps of metric
values with slope vs. alkalinity axes. Automatic determination of optimum weighting of
nearest neighbours was used, and the model was spherical. Values of silt and alkalinity were
scaled to produce diagrams which were of the same width and height.
Ten impacted test sites, also used within a previous CBAS analysis, were selected for
determination of the appropriate interpolated reference condition to which they would be
compared. The interpolated metric values and error values from interpolation were compared
with that from the fixed typology initially used within CBAS (Dodkins et al. 2005a) (which
utilised a more detailed fixed typology than now in place, based on slope, alkalinity,
catchment area and percentage sandy geology within the catchment).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 61
Results
Figure 5 shows the output from ARCMap krigging using CBAS derived metric values and the
CBAS 32 reference sites.
As expected, errors are lower where reference sites are more highly clustered (Figure 5ii).
The patchy nature of the krigging surface is not evident in Figure 5i, but the level of
patchiness did increase with subsequent metrics. This is expected since the silt metric is
most highly correlated with the species data and subsequent species results (metric values)
will be inter-correlated with this metric. Table 5 shows that the krigging reduced errors with all
of the sites tested; around 25% smaller than those produced through a fixed classification.
There are sometimes large differences between the fixed typology silt metric prediction and
the krigged metric prediction.
Table 5. Silt metric reference values for 10 test sites derived from interpolated (krigged)
reference values and fixed typology reference values. Silt metric values and error values
measured in species turnover units.
test
site
no.
slope
(m/m)
alkalinity
(mg/l
CaCO3)
interpolated
silt metric
interpolated
silt metric
error
fixed
typology
silt metric
fixed
typology
silt metric
error
Reduced error
with
interpolation?
4 0.4 24.8 -0.341 0.213 -0.325 0.307 Y
5 1.7 8.2 -0.491 0.228 -0.367 0.275 Y
10 1.4 80.1 -0.127 0.231 -0.325 0.307 Y
13 0.6 90.5 0.071 0.223 -0.367 0.275 Y
23 5 64 -0.236 0.220 -0.240 0.336 Y
55 0.3 170.8 -0.306 0.245 -0.143 0.372 Y
56 0.3 180.3 -0.343 0.251 -0.143 0.372 Y
57 0.6 166.7 -0.281 0.239 -0.325 0.307 Y
95 1.3 117.2 -0.158 0.220 -0.367 0.275 Y
97 0.5 114.8 -0.087 0.223 -0.367 0.275 Y
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 62
Figure 5 Reference conditions for Northern Ireland interpolated through krigging for silt
metric (from CBAS). Showing both the predicted reference metric value (i) and the
associated error value (ii). Crosses represent locations of each of the 32 reference sites used
to create the model.
Key Reference site silt metric prediction Error values
(both in species turn-over units)
Discussion
Interpolation through krigging reduced the error values associated with a fixed typology and
therefore will improve the sensitivity of the CBAS method. The testing procedure was
particularly harsh since the fixed typology utilised a larger suite of variables. Interpolated and
fixed typology metric predictions often varied widely. An examination of the reference site
metric values within the krigging surface suggested that the krigging was more accurate.
slope slope
alka
linity
i. Silt metric - krigged ii. Silt metric - error values
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 63
Although mathematically complex, the 2-dimensional diagrams produced are very easy to
understand. Contour maps of the error associated with these values can be produced may
also help to identify areas where additional reference sites may be required (although not all
combinations of silt and alkalinity would be expected, such as high slope, high alkalinity).
Krigging is recommended for reference site interpolation within CBAS, utilising slope and
alkalinity predictive variables (which are within the current lake and river typologies). Full
testing of krigging is still required, and should be done prior to submission of the CBAS
method. The development of a simple krigging algorithm to incorporate into software would
also be useful to simplify the whole CBAS procedure.
Report Summary Currently there is no perceived advantage in the use of db-RDA, absolute abundances or in
combining environmental gradients to produce metrics from CBAS. It is not suggested that
these are pursued further.
CCA was shown to be preferable to DCA for developing species-environment models that
are used to measure ecological status.
The only major improvements recommended for CBAS before December 2005 is the use of
krigging to interpolate reference conditions. In initial studies this has been shown to provide a
large improvement for assessing ecological status with CBAS.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 64
Developing the new CBAS Model February 2006
Introduction CBAS is an acronym for CCA (Canonical Correspondence Analysis) Based Assessment
System. The original version of CBAS for macrophytes in rivers used survey results for 273
river sites in Northern Ireland to create a CCA model (Dodkins et al. 2005a). Improvements
to the original CBAS method were suggested in “Review of methods to assess the ecological
status of rivers and lakes using macrophytes” (Dodkins and Rippey 2005a). This report
details the redevelopment of CBAS to produce a simple and accurate method of assessing
ecological status (using macrophytes) and to ensure the new method, is Water Framework
Directive (WFD) (Council of the European Communities 2000) compliant. To distinguish the
CBAS river model from the CBAS lake model, it will be referred to as CBASriv in this chapter.
The chapter is divided into 4 sections:
1. Creating the CBASriv Model;
2. Optimising the use of abundance and tolerance in CBASriv;
3. CBASriv Reference Conditions;
4. EQR calculation in CBASriv;
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 65
1. Creating the CBASriv Model 1.1 Introduction The original CBAS used matching macrophyte and environmental data from 273 sites in
Northern Ireland. More data was made available in 2005 (Table 1) for further development.
Species optima in CBAS are developed along environmental gradients within a CCA
ordination, which forms the species-environment model.
Table 1. Available river macrophyte data, and data used in the new CBASriv model
Source Location no.
sites
no.
useable
sites
Reason for exclusion
Original EHS
data (from
original CBAS)
North 273 273 -
RIVTYPE South 50 50 -
Dodkins’
Thesis
North 32 32 -
Additional EHS
data
North 256 165 190 sites did not have exact
matches with both chemical and
hydromorphological data
RIVCON North 300 0 Sites did not have exact
matches with both chemical and
hydromorphological data
Ronan
Matson’s data
South 60 0 Used for external validation
Intercalibration
sites
North &
South
? 0 Data not available
TOTAL: 520
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 66
1.2 Methods Site matching The site matching computer algorithm in the LEAFPACS project produced many non-credible
matches between biological and environmental data. This would severely compromise a
CBAS approach, and therefore it was ensured that environmental and biological survey
locations had identical six figure Irish Grid References (IGRs), providing a total of 520 sites
(Table 1).
Screening appropriate sites Species that are correlated with an impact but are not causally related to it, on a site-by-site
basis, could produce a response that is taken to be due to an impact where there is no
impact. For example Agrostis stolonifera could be correlated with pollution of lowland rivers
whereas its presence may actually be due to local farming landuse. Thus, even if a certain
patch of farmland were not polluting, this indicator species would exist and falsely show an
impact. To some extent this non-casual relationship exists within many metrics, including the
MTR, although an effort to reduce this is necessary in order to produce a robust method that
not only gives good correlations of metrics with impacts, but is also reliable on a site-by-site
basis.
To improve the cause-effect relationships for the species used within CBASriv only aquatic
macrophyte species that were found within the river channel (at least the roots submerged)
were utilised to develop the CBASriv model, and marginal and terrestrial species that are not
typically associated with the river channel but may have been found there (possibly due to
high water level) were removed. A previous report on minimum species lists was used to
help make these judgements (Dodkins and Rippey 2005b). Species which were not in the
Mean Trophic Ranking (Holmes et al. 1999) or Mean Flow Ranking (MFR) (Environment
Agency 2002) species lists were considered for exclusion (Table 2).
In the rare instances where species had only been identified to genus level, these were
retained. It was thought that determination of a genus level optimum would be useful if there
were difficulties in identifying species in the future. Genus optima were generated for
Callitriche, Carex, Chara, Cladophora, Hygrohypnum, Racometrium and Sphagnum.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 67
Table 2. Species not included in the CBASriv model and the reason for inclusion or
exclusion.
Justification for
Species INCLUSION DELETION
Callitriche platycarpa may be a useful indicator
Callitriche stagnalis may be a useful indicator
Diatomaceous algae* may be a useful indicator
Lyngbia* may be a useful indicator
Orthotrichum rivulare may be a useful indicator
Potamogeton filiformis may be a useful indicator
Potamogeton salicifolius may be a useful indicator
Schistidium alpicola
if Cinclidotus is included in
MTR, so should this be
Vaucheria*
an alga, but included due
to good indicator ability
Veronica beccabunga may be a useful indicator
Caltha palustris usually marginal
Conocephalum conicum
splash zone spp. -
excluded
Fissidens spp usually marginal
Glyceria fluitans usually marginal
Glyceria plicata usually marginal
Glyceria spp. usually marginal
Hydrocotyle vulgaris usually marginal
Lunularia crocata
splash zone spp. -
excluded
Marchantia polymorpha
splash zone spp. -
excluded
Petasites hybridus
Helophyte therefore
omitted
Phalaris arundinacea
Helophyte therefore
omitted
Polygonum hydropiper usually marginal
Polygonum persicaria usually marginal
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 68
Table 2. (Cont)
Potamogeton lanceolatus
good indicator but very
rare and may bias the
CBAS model
Potentilla palustris usually marginal
Riccardia
splash zone spp. -
excluded
Riccia glauca
splash zone spp. -
excluded
Sium latifolia usually marginal
Sponge*
not justified as a
macrophyte
Veruccaria spp.*
not justified as a
macrophyte
*these are likely to be included within the phytobenthos survey and are therefore generally
excluded unless large cover of the species is likely to influence the development of other
macrophytes.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 69
Creating the CBAS Minimum Adequate Model (MAM) Full details of the procedure and logic of creating a CBAS model can be found in (Dodkins et
al. 2005a). 4th root species transformation was applied to the % cover species data to
improve the correlations between the CBASriv metrics and environmental gradients (see
Section 2). In small data sets rare species may comprise most of the data, but in larger data
sets rare species are likely to be less important. It was also considered that rare species in
this data set may be biogeographically isolated and therefore downweighting of species was
considered appropriate in the multivariate analysis.
Detrended Correspondence Analysis (DCA) showed that the gradient lengths for the first 4
axes were: (1) 5.704, (2) 4.475, (3) 4.318, (4) 5.445. All of these are greater than 4,
suggesting a unimodal rather than linear model be used in the analysis (ter Braak and
Verdonschot 1995). Thus Canonical Correspondence Analysis (CCA) was applied, using
manual forward selection to include environmental variables in the model that explained the
most additional variance and were significant at P = 0.05 (Bonferonni corrected (Legendre
and Legendre 1998)).
1.3 Results Figure 1 shows the location of the 520 sites used to build the model and the source of the
data. Optima for 101 species were calculated in the CBASriv model. Table 3 shows the
environmental variables used in the development of CBASriv and the transformation of the
variables prior to performing forward selection.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 70
Figure 1. Location of the 520 river sites used to produce the CBASriv model and the source
of the data
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 71
Table 3. Environmental data available for the development of the CBASriv model and the
transformations used. Transformations chosen to approximate normality.
Environmental Variable Abbreviation data
transformation
for model
Altitude ALT log
Width WIDTH log
Depth DEPTH log
Estimated stream power POW log
Local channel slope SLOPE log
Mean Substrate Diameter
(phi) SUBS
none (already
log)
% Silt within channel SILT 2√
% Sand within channel SAND 2√
% Pebbles within channel PEBB 2√
% Boulders within channel BOULD 2√
% Dissolved Oxygen DO 2√
Alkalinity ALK log
pH
PH
none (already
log)
Soluble Reactive
Phosphate SRP log
Nitrate NO3 log
Nitrite NO2 log
Ammonia NH4 log
Conductivity COND log
The four data sets that were combined to develop the new CBASriv model (Figure 2) show
that, generally, silt, slope, pH, alkalinity and nitrate are important for the species composition
of river macrophytes. The RIVTYPE ordination (Figure 2i) lacks a strong nutrient gradient
since all the sites were considered to be in or close to reference condition. The model with
Dodkins’ thesis data (Figure 2ii.) has few variables since there were only 32 sites and this
resulted in low significance levels when testing the variables in forward selection.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 72
i. EHS 1998 MAM ii. Dodkins Thesis MAM
iii. RIVTYPE MAM iv. EHS additional MAM
Figure 2. CCA site conditional biplot MAMs of the four data sets used to develop the
CBASriv model. Down-weighting of rare species and 4th root transformation of species were
used and only variables that explain significant additional variance are included. Inset is a
chart of eigen values for the 1st four axes.
Table 4 shows the variance explained in the new CBASriv model by each variable
individually (marginal effects). Soluble Reactive Phosphate (SRP) concentration explained
the highest variance individually. It is likely that, although the data sets which form the new
CBASriv model show SRP to be unimportant (Figure 2), a much larger range of trophic
status and thus a larger SRP gradient occurred due to the combination of high quality
RIVTYPE sites with impacted sites in Northern Ireland. It may also be that, over a larger data
set, temporal variation in SRP concentration and some poorer quality data may have become
less important than within the separate data sets.
-1.5 2.0
-1.0
1.5
SLOPESILT
DO
ALKPH
NO3
-1.0 2.5
-1.5
1.5
SILT
PH
NH4
-2.0 2.0
-1.5
2.0
WIDTH
SLOPE
SILT
ALKPH
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4
-1.5 1.5
-1.5
1.0
ALT
SLOPE
DO
ALK
NO3
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 73
SRP provided the highest amount of explained species variance, and thus normally would
have been the first variable selected. However, selecting SRP first removed the covariance
with alkalinity, greatly reducing the remaining variance explained by alkalinity. The
importance of alkalinity in the individual data sets (Figure 2) suggest the co-variance
between alkalinity and SRP may be mostly due to alkalinity. Therefore alkalinity was included
first in the model. Also, it is known that both SRP and alkalinity are important in species
distributions, and despite similar species responses, both have to be included to optimise the
ability to distinguish the natural from the impact response. After alkalinity, slope was also a
forced selection, since it is also a typology variable (Environmental Protection Agency
2005a).
The results of the forward selection are presented in Table 5. A choice has to be made
between SILT and SUBS gradients as they were very similar in the direction of the gradient
and the amount of variance explained. SUBS (substrate diameter) produces a continuum of
values along the gradient and utilises data from four different substrate categories.
Therefore, SUBS was expected to describe the gradient more accurately than SILT.
Due to the large number of sites, the significance values were high and the point was
reached where 9,999 permutations (the maximum available in CANOCO) could not
determine if a variable was significant when Bonferroni corrected. Thus, although the 10th
variable, nitrite, achieved a P-value of 0.0001, it was impossible to judge whether this was
significant or not after Bonferroni correction. Thus, nine variables were considered sufficient
for this model.
Table 6 shows the summary table from CANOCO for the CBASriv model. Figure 3 (sites)
and 4 (species) show the final CBASriv ordination model. The first seven axes in this model
are significant (Table 7), although there is a large decrease in variance between axes 4 and
5. Table 8 shows the correlations between the gradients within the CBASriv model.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 74
Table 4. Marginal analysis (individual variance explained) of environmental variables
available for developing the CBAS model. Total species variance (inertia) = 6.592.
Variable Eigen-value (λ1) P-value (9,999 permutations)
Significant?
MTR 0.370 0.0001 YES
MFR 0.347 0.0001 YES
EUTRO 0.280 0.0001 YES
SRP 0.267 0.0001 YES
NO2 0.258 0.0001 YES
ALK 0.235 0.0001 YES
SLOPE 0.218 0.0001 YES
SUBS 0.215 0.0001 YES
NH4 0.199 0.0001 YES
DO 0.198 0.0001 YES
SILT 0.196 0.0001 YES
PH 0.178 0.0001 YES
BOULD 0.175 0.0001 YES
NO3 0.144 0.0001 YES
NORTH 0.125 0.0001 YES
EAST 0.118 0.0001 YES
DEPTH 0.114 0.0001 YES
COND 0.100 0.0001 YES
ALT 0.088 0.0001 YES
WIDTH 0.085 0.0001 YES
SAND 0.083 0.0001 YES
PEBB 0.045 0.0001 YES
POW 0.041 0.0001 YES
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 75
Table 5. Environmental variables that explained the most additional variance (and were
significant at P = 0.05 with Bonferroni correction) and were used to form the CBASriv
Minimum Adequate Model. Total species variance (total inertia) = 6.592.
Variable Eigen-value
(λA)
P-value from 9,999 permutations
Required significance for P = 0.05 with Bonferroni correction
Significant?
ALK 0.235 0.0001 0.05 YES
SLOPE 0.147 0.0001 0.025 YES
SRP 0.122 0.0001 0.0125 YES
SUBS 0.105 0.0001 0.00625 YES
NO3 0.068 0.0001 0.003125 YES
PH 0.061 0.0001 0.001563 YES
DO 0.049 0.0001 0.000781 YES
NH4 0.045 0.0001 0.000391 YES
WIDTH 0.041 0.0001 0.000195 YES
Table 6. CANOCO summary table of CBASriv model (520 sites, variables listed in Table 7).
Axes 1 2 3 4 Total inertia
Eigenvalues : 0.377 0.186 0.089 0.081 6.592
Species-environment
correlations: 0.839 0.75 0.58 0.628
Cumulative percentage
variance
of species data: 5.7 8.5 9.9 11.1
of species-environment
relation: 43.2 64.6 74.9 84.1
Sum of all eigenvalues 6.592
Sum of all canonical
eigenvalues 0.872
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 76
Table 7. Significance of ordination axes in CBASriv model. Axes in bold are those used
within CBASriv for calculating total ecological change (Section 4).
Axis
1 2 3 4 5 6 7 8 All
Eigen-value
0.377 0.186 0.089 0.081 0.042 0.039 0.029 0.026 0.872
P-value
(9,999
permutations
)
0.0001
0.0001
0.0001
0.000
1
0.000
1
0.000
1
0.000
2
0.001
4
0.000
1
Table 8. Weighted correlation matrix of CBASriv model from CANOCO. Ax1 to Ax4 are the
environmental ordination axes in the model. Grey shading indicates correlations between
environmental variable gradients within the model.
Ax1
Ax2 0.00
Ax3 0.00 0.00
Ax4 0.00 0.00 0.00
WIDT
H 0.35 0.16 0.25 0.15
SLOP
E -0.72 -0.18 0.11 -0.26 -0.43
SUBS 0.64 0.44 -0.48 0.12 0.17 -0.50
DO -0.65 -0.18 0.19 0.48 -0.13 0.42 -0.35
ALK 0.67 -0.52 -0.24 0.27 -0.04 -0.36 0.34 -0.31
PH 0.35 -0.79 -0.28 0.24 -0.03 -0.12 0.10 0.04 0.76
SRP 0.80 -0.21 0.30 -0.28 0.13 -0.37 0.26 -0.61 0.52 0.23
NO3 0.53 0.09 0.39 0.37 -0.02 -0.21 0.28 -0.11 0.44 0.24 0.39
NH4 0.63 -0.24 0.12 -0.66 0.10 -0.29 0.14 -0.64 0.40 0.15 0.76 0.06
Ax1 Ax2 Ax3 Ax4
WIDT
H
SLOP
E SUBS DO ALK PH SRP NO3
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 77
Figure 3. CBASriv ordination model. Site conditional biplot of axes 1 and 2. The first two
axes explain 64.6 % of the (total canonical) variance.
Figure 4. CBASriv ordination model. Species conditional biplot of axes 1 and 2. Only species
with a fit of 2% or greater are shown. The first two axes explain 64.6 % of the (total
canonical) variance.
-1.0 1.0
-1.0
1.0
WIDTH
SLOPE
SUBS
DO
ALK
PH
SRP
NO3
NH4
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6 7 8
-1.0 1.0
-1.0
1.0
alis pla
ambl rip
brac plubrac rivcall cus
call ham
call obt
call spp
call sta
chil pol
cinc fonclad spp
cono con
dich pel
elod canfila gre
font squ
hild riv
hygr spp
lemn min
lemn pol
litt uni
marc pol
mars ema
nuph lut
pell end
pell epi
peta hyb
phal aru
pota natraco sppranu fla
ranu pel
rhyn rip
scap und
schi alpsole
spar eme
spar ere
tham alo
WIDTH
SLOPE
SUBS
DO
ALK
PH
SRP
NO3
NH4
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6 7 8
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 78
1.4 Conclusions Within the separate data sets alkalinity, slope, substrate, nutrient concentration and pH were
most strongly correlated with macrophyte species. All these gradients were included in the
new CBASriv model. More species variance is explained within the new CBASriv model
compared to the previous CBAS model (Dodkins et al. 2005a) i.e. species variance
explained is: CBASriv (13.2 %) and original CBAS (9.3 %); total (canonical) variance
explained by the first two axes explain: CBASriv (64.6 %) and original CBAS (54.9%);
percentage of total species variance (total inertia) explained by the first two axes is: CBASriv
(8.5%) and original CBAS (5.1%). CBASriv is based on twice the number of river sites of the
original CBAS model, utilises more environmental variables, and the sites cover a wider
geographical area. Therefore CBASriv is likely to represent the environmental gradients
more accurately.
Separating the response of macrophytes to alkalinity from their response to anthropogenic
nutrient enrichment is important in the development of a method to measure ecological
status. This was achieved by including both variables within the model (CCA maximises
niche separation). The correlation between alkalinity and SRP in explaining species variance
(from CANOCO’s weighted correlation matrix) was only 0.52 (Table 10). Combined with the
use of reference conditions to estimate the expected SRP metric value at a particular
alkalinity, this suggests that the SRP metric generated from the CBASriv model should be
quite distinct from an alkalinity gradient.
The correlation between SRP and ammonia is quite high (0.76) and thus the metrics derived
from these gradients may respond in a similar way to either impact pressure. However all the
variables in CBASriv explain significant additional variance and therefore, in a small number
of sites, the responses will distinguish different types of impact. The correlation between the
main nutrient gradient (SRP) and the substrate gradient (SUBS) was higher (0.26) than in the
original CBAS model (0.20), although it will still be likely that there is reasonable distinction
between nutrient and siltation impacts. The original CBAS model only had a nitrate gradient
reflecting nutrient impacts. CBASriv has three nutrient gradients from which metrics can be
generated (SRP, NH4, NO3). The SRP gradient is particularly strong, making CBASriv more
responsive to nutrient impacts.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 79
2. Optimising the Use of Abundance and Tolerance in CBASriv 2.1 Introduction Species optima are derived from the CBASriv ordination model. The SPEC_ENV file from
CANOCO (ter Braak and Smilauer 2002) provides accurate multivariate optima along each of
the gradients used in the model. Niche breadths for each species, however, are only
available along the ordination axes and not the environmental variables. Therefore, an
estimate of niche breadth for species along each environmental gradient is determined by
using an ordination with each variable individually in separate CCA ordinations. Since CCA is
a constrained analysis, the first axis is exactly correlated with the environmental variable
when a single variable is used. Therefore, the tolerances of species for axis 1 (found in the
CANOCO solution file) are good estimates of the niche breadth along that environmental
gradient.
To determine the ecological impacts along environmental gradients, metrics are determined
using a weighted averaging equation. This can be considered equivalent to the method used
to calculate the MTR score, although with an additional weighting from the reliability of the
species as an indicator of position on the gradient. This indicator value is derived from the
niche breadth; however, as a smaller niche breadth suggests that a species is a better
indicator of a point along a gradient, a species with a small niche breadth should have a
larger weighting value. Since niche breadths were no greater than 3 (chi2 dissimilarity units),
the indicator value was 3 minus the niche breadth.
The weighted averaging equation used to determine the metric value along an impact
gradient is:
Where:
E = metric value (estimated value of the environmental variable, measured as chi2
dissimilarity)
ai = abundance of ith taxa at the site (fourth root of percentage cover)
si = optimum of the ith species (sensitivity)
vi = indicator value for the ith species (derived from niche breadth)
Within the new CBASriv model, impact metrics can be calculated for:
∑∑=
ii
iii
vavsa
E(Equation 1)
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 80
SRP (Soluble Reactive Phosphate) concentration;
SUBS (Mean substrate diameter) with a higher value indicating more silty conditions;
NH4 (Ammonia) concentration;
DO (Dissolved Oxygen) saturation;
PH (pH) value and;
NO3 (Nitrate) concentration
Weighting of the species optima by abundance and niche breadth in the weighted averaging
equation (Equation 1) does not derive directly from the CBASriv model and so the values of
these properties may not be optimised. For example, the scale chosen for indicator value
could be considered arbitrary, since niche breadth actually represents a species distribution
curve. This section is an investigation into the use of species abundance in the creation of
the CBASriv model, and the optimisation of the weighted averaging calculation to improve
the correlation between the impact predicted from the metric and the actual impact.
2.2 Method Obtaining external validation data Since all the available river data was used in creating the model, no data was available for
external validation. Therefore, the 520 sites were randomly split into two data sets of 260
sites following (Hallgren et al. 1999). One data set (called SPLIT-MODEL) was used to
create optima and niche widths using the same environmental gradients as the CBASriv
model described in Section 1. The remaining 260 sites were used to test the model. An
internal validation with all 520 sites is referred to as the CBASriv model, whereas the
validation model with only 260 sites is referred to as SPLIT-model and the validation data set
as SPLIT-validation.
Optimising abundance transformation in the CBAS model The previous version of CBAS used square-root transformation of % species cover to
produce the model (Dodkins et al. 2005a). With CBASriv, no transformation, square root,
fourth-root and presence/absence species transformations were tried to determine which
transformation produces the highest explained variance within the model. In addition, a
categorical data transformation was applied, in accordance with CEN (Table 1).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 81
Table 1 Categorisation of macrophyte abundance data according to (CEN 2003b).
Categorical
value
Visual cover
estimate (% of
channel or bank)
0 0
1 < 0.1
2 0.1 - 1
3 >1 - 5
4 >5 - 10
5 > 10
Optimising abundance transformation in the CBAS metric calculation The CBASriv model with 4th root species transformation and down-weighting of species was
found to explain most variance (Table 2), so the effect of abundance weighting within the
calculation of the metrics (Equation 1) was compared with the results of this model. Square-
root, 4th-root and omission of abundance (presence/absence) weighting was used to
determine the metric values for all 520 sites in CBASriv. The square of the Pearson’s product
moment correlation coefficient, more commonly known as the coefficient of determination (r2
value), was calculated from the linear regression between the metric value (calculated from
the species data) and the value of the associated environmental variable, for each of these
transformations. Significant differences (at P = 0.05) between different abundance
transformations were tested using the chi-squared test of z-values (Edwards 1976). Both
internal and external validation methods were used to assess metric performance.
Since the % cover species transformation used in the model may affect which is the optimal
species transformation in the metric calculation, 2nd and 4th root transformed models were
each tested with both 2nd and 4th root transforms in the metric calculation.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 82
Optimising indicator values within the metric calculation
Scenario manager within Microsoft Excel was used to enable the indicator value to be
optimised through the repeated permutations of different values for the constants within the
following equation:
ctqkv ×−=
Where:
v = indicator value
t = tolerance (niche breadth) from CANOCO
k, q, c = constants which could be permuted within Scenario Manager
Through a permutation process, constants k, q and c were changed in order to maximise the
r2 values of the regression of the SRP metric against the underlying environmental gradient
(soluble reactive phosphate concentration).
Each of the different species abundance transformations (4th root, 2nd root and presence-
absence) within the metric calculation were again assessed using linear regression of the
metric against the underlying environmental gradient, but this time either using indicator
values or not using them. This was to ensure that the best combination of tolerance and
abundance weighting would be used.
Equation 2
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 83
2.3 Results
The SPLIT-model was very similar to that of the complete CBASriv model, with the nine
environmental variables from the CBASriv model still being significant at P = 0.05 (including
Bonferroni correction).
The model with down-weighting of rare species and 4th root transformation of % species
cover had the highest explained percentage species variance (Table 2).
Table 2. Variance in species data explained by the nine variables in the CBASriv model
when different species transformations are used. Down-weighting indicates the
‘downweighting of rare species’ option in CANOCO.
Species transformation
None Square-root
Fourth-root Categorical
Presence/absence
Without down-weighting
Total variance explained by
variables 1.871 1.355 1.13 1.175 1.036
Total species variance (total inertia) 20.374 13.914 11.506 12.565 12.46
% total species variance explained 9.2 9.7 9.8 9.4 8.3
With down-weighting
Total variance explained by
variables 1.367 1.03 0.872 0.865 0.713
Total species variance (total inertia) 15.003 8.715 6.592 6.901 6.119
% total species variance explained 9.1 11.8 13.2 12.5 11.7
4th root transformation of species abundance in the calculation of metric values resulted in
higher correlations with the underlying environmental gradients than square root
transformation (Figure 1). With internal validation, five out of the nine metrics, not using any
abundance weighting (just presence/absence) produced the highest correlation values. The
exceptions tended to be physical habitat metrics, i.e. SUBS, SLOPE and DO. The same
pattern is evident with external validation (Figure 2) i.e. there are significant increases in the
r2 value for ALK, NH4, NO3 and WIDTH when no abundance weighting is used. The
increased r2 value for the SRP metric was not significant, and PH, SUBS, SLOPE and DO
metrics (mostly physical metrics) had a small but significant decrease in r2 value with no
abundance weighting. The mean increase in r2 value over all the metrics (with external
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 84
validation) is 0.030 from 2nd root to 4th root, and 0.004 from 4th root to no abundance
weighting.
A fourth root abundance model and 4th root species transformation generally produced
metrics with the highest correlation against their underlying gradients (Figure 3). Metrics with
no abundance weighting are not illustrated in this figure, but they have been shown to be
better with nutrient metrics than 4th root transformation, in Figure 2.
The permutation process used to optimise the niche breadth resulted only in a very minor
improvement in the r2 value of 0.01 when constant k was extremely large (100,000,000).
Changes to constant q had no effect. The optimal value for constant c, where tolerance
raising to the power of 0.3, produced a minor improvement in r2 of 0.04. The results for
constants k and c suggest that the severe down-weighting of the tolerance values improves
the correlation.
Figure 4 shows that, except for SUBS, SLOPE and DO (physical metrics), the use of
indicator and abundance weighting produces the same or worse metric performance than
using neither abundance nor tolerance. Figure 5 shows a similar pattern i.e. indicator values
provide only slight benefits for SRP, NH4, DO and NO3 metrics, and Abundance weighting
provides only slight benefits for PH, SUBS, SLOPE and DO metrics.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 85
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ALK
SR
P
PH
SU
BS
NH
4
SLO
PE
DO
NO
3
WID
TH
r2 val
ues 2nd root
4th rootpres/abs
r2 values ALK SRP PH SUBS NH4
SLOP
E DO NO3
WIDT
H
2nd root 0.588 0.579 0.527 0.462 0.457 0.437 0.417 0.278 0.236
4th root 0.617 0.607* 0.565 0.471 0.480 0.447 0.435 0.335 0.258*
pres/abs 0.625 0.607* 0.570 0.454 0.473 0.434 0.428 0.372 0.260*
Figure 1. Different species abundance transformations within the metric calculation: r2 values
for the regression of CBASriv metric values against the values of the properties that
represent the underlying environmental gradients (internal validation). ‘Pres/abs’ means
presence/absence species results, i.e. there was no abundance weighting. (*) Pairs not
significantly different at P = 0.05.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 86
0.0
0.1
0.2
0.3
0.4
0.5
0.6
ALK
SRP
PH
SUBS NH
4
SLO
PE DO
NO
3
WID
TH
r2 val
ues 2nd root
4th rootpres/abs
ALK SRP PH SUBS NH4
SLOP
E DO NO3
WIDT
H
2nd root 0.501 0.480 0.477 0.295 0.433 0.354 0.293 0.205 0.178
4th root 0.536 0.511* 0.508 0.314 0.485 0.369 0.303 0.257 0.208
pres/abs 0.545 0.515* 0.497 0.307 0.499 0.356 0.284 0.299 0.226
Figure 2. Different species abundance transformations within the metric calculation: r2 values
for the regression of CBASriv metric values against the values of the properties that
represent the underlying environmental gradients (external validation). ‘Pres/abs’ means
presence/absence species results, i.e. there was no abundance weighting. (*) Pairs not
significantly different at P = 0.05.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 87
0.0
0.1
0.2
0.3
0.4
0.5
0.6
ALK
SR
P
PH
SU
BS
NH
4
SLO
PE
DO
NO
3
WID
TH
r2 val
ues 2M, 2A
4M, 2A2M, 4A4M, 4A
ALK SRP PH SUBS NH4
SLOP
E DO NO3
WIDT
H
2rtM, 2rtA 0.500a 0.490 0.473d 0.287 0.444 0.351g 0.300 0.212 0.166
4rtM, 2rtA 0.501a 0.480 0.477d 0.295 0.433 0.354g 0.293 0.205 0.178
2rtM, 4rtA 0.534b 0.516c 0.506e 0.307 0.490f 0.362 0.306h 0.268 0.194
4rtM, 4rtA 0.536b 0.511c 0.508e 0.314 0.485f 0.369 0.303h 0.257 0.208
Figure 3. 4th (4rt) and 2nd root (2rt) species abundance transformations within the within the
model (M) and within the metric calculation (A): r2 values for the regression of CBASriv metric
values against the values of the properties that represent the underlying environmental
gradients (external validation). Superscript letters e.g. (a) indicates pairs of r2 values which
are not significantly different at P = 0.05.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 88
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ALK
SRP
PH
SUBS NH
4
SLO
PE DO
NO
3
WID
TH
r2 val
ues
indicator values and abundance no indicator valuesno abundance no indicators or abundance
ALK SRP PH SUBS NH4 SLOPE DO NO3 WIDTH
ind. &
abund. 0.617a 0.607 0.565b 0.471 0.480 0.447 0.435 0.335 0.258e
no ind. vals 0.616a 0.600 0.564b 0.460 0.502 0.437c 0.428d 0.325 0.256e
no abun. 0.625 0.607 0.570c 0.454 0.473 0.434c 0.428d 0.372 0.260e
no ind or
abun 0.618 0.596 0.565c 0.443 0.497 0.420 0.418 0.357 0.256e
Figure 4. Omission of indicator values and/or species abundance weighting in the metric
calculation within the 4th root model: r2 values for the regression of CBASriv metric values
against the values of the properties that represent the underlying environmental gradients
(internal validation). Superscript letters e.g. (a) indicates pairs of r2 values which are not
significantly different at P = 0.05.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 89
0.0
0.1
0.2
0.3
0.4
0.5
0.6
ALK
SR
P
PH
SU
BS
NH
4
SLO
PE
DO
NO
3
WID
TH
r2 val
ues
indicator values and abundance no indicator valuesno abundance no indicators or abundance
ALK SRP PH SUBS NH4 SLOPE DO NO3 WIDTH
ind. &
abund. 0.536a 0.511 0.508 0.314 0.485 0.369d 0.303 0.257f 0.208g
no ind. vals 0.536a 0.501 0.521 0.307c 0.469 0.368d 0.292 0.253f 0.207g
no abun. 0.545b 0.515 0.497 0.307c 0.499 0.356e 0.284 0.299 0.226h
no ind or
abun 0.542b 0.504 0.514 0.301 0.493 0.355e 0.275 0.290 0.225h
Figure 5. Omission of indicator values and/or species abundance weighting in the metric
calculation within the 4th root model: r2 values for the regression of CBASriv metric values
against the values of the properties that represent the underlying environmental gradients
(external validation). Superscript letters e.g. (a) indicates pairs of r2 values which are not
significantly different at P = 0.05.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 90
2.4 Conclusions
The use of fourth-root transformation of species abundance values and down-weighting of
species results in the highest amount of species variance explained by the environmental
variables in the CBASriv model. This model is therefore recommended (Section 1).
The categorical species transformation used in EN14184 (CEN 2003b) produced similar
variance explained to the 4th root transformation. It is likely that either categorical results
and/or 4th root transformed results could be used in any further development of CBASriv
models.
Using no abundance weighting in the metric calculation improved the correlation between the
metric and the impact it represents for nutrient metrics (ALK, SRP, NH4, NO3) and WIDTH,
whilst only slight decreases in performance were found with physical metrics (SUBS,
SLOPE, DO) and PH. Abundance measures may provide little additional information since: i.
Much of the survey data here has been taken near bridges with a variety of physical habitats
unrepresentative of the whole reach, ii. Natural variation in intra-seasonal macrophyte
abundance could be large compared to abundance changes due to impacts, iii. Natural
variation in reach scale spatial habitat is large compared to abundances changes due to
impacts, iv. Macrophyte abundance has little relationship with all except the physical impacts
except in extreme cases of eutrophication.
Omitting the indicator value weighting in the metric calculation improved the PH metric, did
not affect the ALK, SLOPE or WIDTH metrics, and only had a minor negative affect on the
SRP, SUBS, NH4, DO and NO3 metric performance. It is suspected that indicator values
derived from the analysis are not representative of the real niche breadth of the species;
possibly because the model will tend to assume that rare species have a small niche
breadth.
It was recommended that, to increase the simplicity of both fieldwork and calculation,
abundance and tolerance weightings should be abandoned and that metric scores in
CBASriv should be based on the mean optima of the species found.
The resultant increase in efficiency of surveying should enable better quality control for
species identification and more representative and/or larger survey sections to be covered,
as well as more useful functional metrics to be developed e.g. total biomass or habitat
diversity metrics. Further work on tailoring the field survey method to suit information
requirements is recommended.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 91
3. CBASriv Reference Conditions
3.1 Introduction
The selection of reference conditions is intrinsic to the determination of ecological status
within the WFD. In the RIVTYPE project, fifty sites that represent high ecological status for
phytobenthos, macrophytes and invertebrates in rivers in the Republic of Ireland were
selected and used to develop a river typology (Environmental Protection Agency 2005c).
Although the sites were confirmed to be of high status and they were included in the new
CBASriv model, it was considered best to identify the highest quality reference sites within
the 520 sites that were used to develop the model and which also covering the Republic of
Ireland and Northern Ireland. It is expected that there will be further discussions on reference
conditions and changes to the reference network as a result of expert judgement within the
EPA and EHS.
There are no distinct macrophyte communities within rivers and therefore any fixed typology
with discrete boundaries will tend to have high errors for the prediction of reference
conditions near the typology boundaries. For example, there is likely to be little difference
between species found at 100 and 101mg/l CaCO3, although the two are considered to be
from different river types within the current WFD river typology for Ecoregion 17 (Table 1).
The typology developed in the RIVTYPE project was optimised to discriminate phytobenthos,
macrophyte and invertebrate species and it is therefore useful as a framework for
determining reference conditions and as a reporting typology. However, interpolating
reference sites to produce monitoring site-specific reference conditions will increase the
sensitivity of metrics to detect impacts.
Two methods were considered to be suitable for reference site interpolation: krigging and
multiple linear regression. Krigging interpolates a characteristic (e.g. a metric value for
reference conditions) using the nearest neighbours. Unlike inverse distance weighting,
krigging allows error values around the predicted value to be calculated (Fortin and Dale
2005). In linear regression the reference metric value is plotted against the predictive
variable (e.g. alkalinity) and a line of best fit drawn between the points. The equation of this
line can be used to generate a reference metric value from any alkalinity value. Multiple
linear regression enables several predictive variables to be used simultaneously (e.g. two
variables would produce a plane of best fit).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 92
3.2 Method Selection of reference conditions Reference river sites had already been selected within the RIVTYPE project (Environmental
Protection Agency 2005c) for the Republic of Ireland and potential macrophyte reference
sites had been selected for Northern Ireland (Dodkins 2003). However, it was considered
that some of the sites within these studies were not of high status. The RIVTYPE sites were
used as the basis for the reference sites, although sites which had been identified as being of
questionable high status (within the RIVTYPE report) were removed, as well as sites
considered to be less than high status based on the survey descriptions and MTR.
Since two replicates of the macrophyte RIVTYPE sites were surveyed, only the best of each
acceptable replicate was initially selected. Additional reference sites were chosen, based on
an assessment of the impact metric responses. Metric scores were calculated for each of the
520 CBAS sites. DO and PH metric values were multiplied by -1 to ensure that an increasing
metric value indicates an impact following (O'Conner et al. 2000). The nutrient metrics (SRP,
NO3, NH4) were combined by decomposing the metric into orthogonal axes, using the
correlation of each metric with the ordination axes, and then adding the maximum metric
score along each axis together (Dodkins et al. 2005a). Physical metrics (DO, SUBS) were
combined in the same way.
For each river type (Table 1), the 10 % of sites with the lowest combined nutrient metric
values were selected to add to the initial selection of reference sites. Therefore naturally silty
rivers in lowland rivers were still included, although the combined physical metric was used
as a secondary criterion to prevent higher slope and lower alkalinity rivers being accepted if
they were physically impacted. This resulted in a total of 68 reference sites (Table 3).
RIVTYPE sites that were not included were reviewed and their exclusion is justified in Table
2.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 93
Interpolating reference conditions with krigging The geostatistical analyst routine in ArcMAP (ESRI 2002) was used to perform krigging (no
trend removal, spherical ordinary krigging with five nearest neighbours) and to produce a
prediction map for the metrics and a prediction map for the standard error of the metrics.
Alkalinity and slope form the reporting typology within Ecoregion 17 and they were also the
most important unimpactable variables within the CBASriv model. Therefore, it was
considered that alkalinity and slope should form the x- and y-axis within the krigged area.
Metric scores were calculated for each of the nine gradients within CBASriv from mean
optima values (see Section 2) at each of the 68 reference sites. The reference typology
(Table 1) uses untransformed alkalinity and slope values, and gives a similar weighting to
each property (similar number of categories). Therefore, untransformed alkalinity was used in
the krigging model, but since the maximum slope value (m/km) was around three times
smaller than maximum alkalinity, slope was multiplied by three so that a square krigging
space would be created, and thus the nearest neighbour calculations would give equal
weight to slope and alkalinity.
Multiple Linear Regression (MLR) Log alkalinity, log slope and log width were used as predictive variables in MLR. Log values
were used since the metrics are closer to a linear relationship with transformed rather than
untransformed values. MLR was performed using SPSS (SPSS Inc 1999) to produce
predictive equations for each of the nine metrics from the predictive variables. For some
metrics, slope or width added little additional increase in the correlation between the
reference site metrics and the predictive variables, and therefore these variables were not
always used in prediction.
3.3 Results Table 1 shows the fixed river typology used within Ecoregion 17, which was used to stratify
the selection of high quality sites.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 94
Table 1. Ecoregion 17 (Ireland) river typology. The type codes have two-digits codes with the
first digit indicating the geology of catchment and the second digit river slope ((Environmental
Protection Agency 2004)).
Code: Catchment Geology
(% bedrock in upstream catchment
by type)
Description Hardness/Alkalinity
1 100% Siliceous Soft water <35 mg CaCO3/l
2 1-25% Calcareous (Mixed Geology) Medium
hardness
35-100 mg
CaCO3/l
3 >25% Calcareous Hard water >100 mg CaCO3/l
Code: Slope (m/m)
1 <=0.005 Low Slope
2 0.005-0.02 Medium Slope
3 0.02-0.04 High Slope
4 >0.04 Very High
Slope
Table 2 shows the justification for RIVTYPE sites that were not included in the work reported
here. Table 3 shows the final list of reference sites used to produce the reference conditions.
A total of 68 reference sites were selected; 35 in the Republic and 33 in Northern Ireland.
Figure 1 shows a gradient map of the krigged metric values for the 68 reference sites,
interpolated using alkalinity and slope values. Figure 2 shows the alkalinity and slope range
encompassed by this krigging model and shows the CBAS reference site numbers. A distinct
patch in the krigged SRP and PH metrics (Figure 1) can be seen around site 325. This site is
the Caher River in the Republic of Ireland, which is highly alkaline for it’s small size and high
altitude. The krigging surface therefore tends to suggest a low nutrient and high pH for the
alkalinity level.
The error maps produced from krigging (Figure 1) follow the density of the krigging surface of
the reference sites. High alkalinity and high slope sites did not exist (except for the Caher)
and therefore error values in the top right corner of the error maps are high. The NO3 metric
has a rather patchy distribution, which may result in quite a poor prediction of the reference
value, as indicated within the error map. However, as with SRP, all the reference sites have
a low NO3 metric compared to the other sites within the CBASriv model and therefore it is
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 95
still likely that an NO3 metric reference value will be sufficiently sensitive and accurate. The
highest NO3 metric value, which may have resulted in some of the observed patchiness, is
found at sites 339 (Dunneil), 387 (Owenmore), 351 (Flesk) and 400 (Sullane). These are all
RIVTYPE sites that were selected prior to evaluation of the metric values. However, the
metric scores are not excessive and therefore this does not necessarily mean they are not at
high status.
The MLR equations with predictive variable coefficients are presented in Table 4, along with
the r2 from the regression of the predicted metric value from this equation against the actual
metric value. Table 2. RIVTYPE sites that were not included in the reference network. (*) indicates sites
that were determined to have possible minor impacts within the RIVTYPE report
(Environmental Protection Agency 2005c).
RIVTYPE site Reason for exclusion
AILLE1 *
BHALL1 possible impact indicated by macrophytes
BILBO1 *
BOLND1 *
BROAD1 slightly nitrate enriched?
CARAG1 *
CLYDA1 *
DUNNE2
cattle grazing, fertiliser bags found, too silty -
impacted?
EANYM2 very few flow types - altered?
FINOW1 *
FUNSH1 high nitrate levels for river type
GCREE1 *
GDINE1 *
GOWLA1 obvious cow access - slight eutrophication?
KEERG1 obvious cow access - poor MTR, better examples
LIFFY1 *
LSLAN2 better examples with less nitrate
OGLIN1 *
OREAG1 *
OWGAR1 *
SHILL1 Poaching and erosion due to cows
SLANY1 *
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 96
Table 3. Reference sites used within new CBASriv. (NB. IRTU is now called EHS) Alkalinity category
Slope category
CBAS no. Source Easting Northing Site name River name25 IRTU 266900 406700 28 Owenrigh Carnabane431 IRTU additional 212400 385700 35 Foyle Secondary315 RIVTYPE 078287 072138 BLKWA1b Blackwater (Kerry)
1 342 RIVTYPE 184528 381562 EANYM1a Eanymore Water343 RIVTYPE 184414 381544 EANYM1b Eanymore Water347 RIVTYPE 183978 381407 EANYW1b Eany Water351 RIVTYPE 106697 085375 FLESK1b Flesk (Kerry)360 RIVTYPE 308817 196238 GNEAL1a Glenealo289 IANS THESIS 201800 343500 169 Black Drumkeenagh186 IRTU 257600 387500 204 Glenlark R. at Glenlark Br.232 IRTU 252700 385500 252 Glenmacoffer Glenmacoffer
1 2 236 IRTU 260300 387000 256 Coneyglen C'Glenra302 IANS THESIS 260300 387000 256 Coneyglen C'Glenra358 RIVTYPE 089680 058347 GGARF1a Glengarriff383 RIVTYPE 183755 168332 NPORT1b Newport (Tipperary)331 RIVTYPE 192936 418910 CBURN1b Cronaniv Burn368 RIVTYPE 194839 413968 GWBAR1a Gweebarra
3 387 RIVTYPE 051329 110655 OMORE1b Owenmore (Kerry)403 RIVTYPE 214894 324635 SWANL1b Swanlinbar404 RIVTYPE 286384 148548 URRN1a Urrin510 IRTU additional 262500 393700 66 Foyle Minor
4 334 RIVTYPE 311123 220168 DODDE1a Dodder374 RIVTYPE 298452 191761 LSLAN1a Little Slaney443 IRTU additional 223600 336700 21 Arney River429 IRTU additional 208300 338400 25 Lough Macnean River441 IRTU additional 223000 341300 29 Sillee's442 IRTU additional 223100 382700 46 Foyle Primary505 IRTU additional 260400 372100 104 Foyle Secondary481 IRTU additional 247200 373700 113 Foyle Primary138 IRTU 238400 347300 154 Manyburns Manyburns Br294 IANS THESIS 324000 432600 191 Glendun Knocknarry
1 185 IRTU 223100 382700 203 Killenburn Glashagh299 IANS THESIS 255200 351700 228 Fury Belalastera238 IRTU 247200 373700 258 Killyclogher at Killclogher250 IRTU 236300 349200 271 Tempo Tattinveer336 RIVTYPE 171928 208982 DUNIR1a Duniry337 RIVTYPE 172120 208970 DUNIR1b Duniry362 RIVTYPE 148104 164137 GOURN1a Gourna
2 363 RIVTYPE 148072 164141 GOURN1b Gourna367 RIVTYPE 155410 190143 GRANE1b Graney (Shannon)400 RIVTYPE 131316 072762 SULLA1a Sullane145 IRTU 213200 336700 162 Cladagh Gorteen290 IANS THESIS 194100 351800 171 Roogagh Garrison293 IANS THESIS 323700 427500 190 Glenarm Cushendall
2 326 RIVTYPE 220175 201381 CAMCO1a Camcor390 RIVTYPE 156870 323190 OWBEG1a Owenbeg (Coolaney)391 RIVTYPE 156921 323167 OWBEG1b Owenbeg (Coolaney)393 RIVTYPE 155476 190069 OWDAL1b Owendalulleegh
3 320 RIVTYPE 166545 187131 BOW1a Bow321 RIVTYPE 166578 187135 BOW1b Bow
4 318 RIVTYPE 182232 347127 BONET1a Bonet319 RIVTYPE 182231 347140 BONET1b Bonet493 IRTU additional 253000 352100 17 Blackwater R Feeder509 IRTU additional 262500 353000 33 Blackwater 452 IRTU additional 232700 372500 91 Foyle Secondary142 IRTU 248200 327200 158 Lackey Knockballymore
1 286 IANS THESIS 213400 344500 166 Boho Boho304 IANS THESIS 237400 330700 277 Lough A Halchu below Moorlough261 IRTU 268900 422800 282 Castle Drummond
3 420 IRTU additional 294200 413700 411 Lower Bann379 RIVTYPE 149298 316786 MOY1b Moy381 RIVTYPE 126182 300838 MOY2b Moy295 IANS THESIS 314100 440600 192 Carey Careymill
2 413 IRTU additional 288900 419100 414 Lower Bann309 RIVTYPE 132503 317098 BEHYM1b Behy (North Mayo)325 RIVTYPE 116322 208228 CAHRE1b Caher (Clare)
3 338 RIVTYPE 143713 334116 DUNNE1a Dunneil339 RIVTYPE 143866 334409 DUNNE1b Dunneil
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 97
ALK
SLOPE SRP
Figure 1. Gradient maps of reference metric value (left) and standard error (right) of reference metric value for each of the metrics in the CBASriv model, based on krigging the metric values of 68 reference sites using alkalinity (x-axis) and slope (y-axis). Darker areas indicate higher metric values (more impacts except in ALK and SLOPE metrics), but the gradient of colour is not linear (it is optimised for display purposes).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 98
SUBS
NO3 PH
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 99
DO
NH4
WIDTH
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 100
Figure 2. Reference site number and x and y-axis scales within the krigging maps of Figure
1.
Table 4. Alkalinity, slope and width coefficients from Multiple Linear Regression, to produce
the expected location of the reference condition along an impact gradient. The predictions
are in chi2 dissimilarity x 10 (to produce correct values for the field method).
Coefficients for predictive variable
log
alkalinity
log
slope
log
width
Constant
Mean
Standard Error
of Prediction
(±)
SRP 3.4 0.4 -10 0.35
NO3 0.8 -0.3 0.9 -5 0.46
NH4 3 -0.3 0.7 -8 0.42
SUBS 1.1 -0.8 1.5 -6 0.30
DO 2 -0.7 -7 0.26
PH -7.3 14 0.45
Alkalinity (mg1 263
Slope
0
78
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 101
3.4 Conclusions
Sixty-eight river sites were used to develop a reference site network and they included 35
(RIVTYPE) sites in the Republic of Ireland and 33 high status sites in Northern Ireland. The
sites cover the range of alkalinity and slope values that were used to form the reporting
typology for rivers in Ecoregion 17.
The krigging gradient maps provide a visual method of interpreting the reference metric value
and enable patterns in the variation of metric values to be examined and site outliers to be
visually identified. However, patterns in expected impact metric values at alkalinity and slope
values tend to be quite uniform (though not linear) with these reference sites, suggesting that
multiple linear regression is quite suitable for prediction.
Although Site 325 (River Caher) is high status, it is unusual because rivers of similar
alkalinity naturally tend to have a higher soluble reactive phosphate concentration and pH.
Retaining the River Caher within the reference sites may have the effect of being too
stringent for the expected SRP and PH metrics at reference conditions in the vicinity of this
site’s alkalinity (175 mg/l CaCO3) value. It was considered feasible to retain this site,
although additional reference sites could be added in future. 3-d krigging could be used to
improve the predictions using e.g. width or optical density, however it is much more difficult
to visualise and present a 3-d krigging.
Krigging has an advantage over multiple linear regression of immediate visual interpretation
enabling reference site gaps or outliers to be easily identified. It also works effectively when
there is not a linear relationship between the distribution of the metric and the predictive
values. However, in this case, there was an approximately linear relationship between the
metrics and the predictors (possibly due to the structure of the ordination model). Thus
multiple linear regression was appropriate. MLR can easily incorporate four or more
environmental predictors and is computationally simpler, enabling reference conditions to be
predicted in the field. Multiple linear regression will therefore be used to produce site specific
reference conditions.
The reference network should be regularly reviewed and improved, ensuring that reference
sites are of high ecological status and represent the range of natural variation. An expert who
knows the specific sites intimately should review the reference sites presented here. It is
suspected that optical density (e.g. Hazen value) of the water would greatly improve
reference condition predictions. pH would also improve predictions, however currently it
cannot be used as a predictor in the WFD.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 102
4. EQR Calculation in CBASriv 4.1 Introduction
Ecological impact “shall be expressed as ecological quality ratios for the purposes of
classification of ecological status. These ratios shall represent the relationship between the
values of the biological parameters observed for a given body of surface water and the
values for these parameters in the reference conditions applicable to that body” (WFD,
Annex V, 1.4.1 ii.).
Thus an ecological quality ratio (EQR) ranges from 0 to 1, where 1 is high status. Using high
scores for reference conditions infers that there will be more of something, for example,
pollution sensitive species, at high status. However, the presence of a species (particularly
with macrophytes) can be more indicative of an impact rather than a reference state. Indeed,
in CBAS, the assumption of ‘good’ and ‘bad’ species is not used and the philosophy of it’s
development assumes that all macrophyte species spent most of their evolution adapting to
the natural conditions of the aquatic environment. Human impacts create artificial
environments that reflect certain aspects of natural conditions (e.g. a eutrophic watering
hole), thus species were placed on an environmental gradient in CBAS, rather than
considered high or low status species per se.
The metrics could simply be added together to produce a score of total ecological change
(TEC), measured in chi2 units of ecological distance, and from this the EQR could be
calculated. However correlations between the metric values would result in this being highly
inaccurate; with the TEC being dependent on the number of metrics used in the model, the
metrics chosen, and the strength of correlations between metrics. A more complex method of
metric combination was previously developed with CBAS (Dodkins et al. 2005a). However
there was potential for developing a simpler method for field calculation, and a more accurate
method for the computer. This section details the two new methods for determining TEC
(ecological change, measured in relevant ecological change units) and from this, the EQR.
4.2 Methods Determination of total ecological change (TEC) - field method
For each of the six impact metrics (SRP, NO3, NH4, SUBS, DO, PH) the mean metric optima
of the species that occur at the site is calculated to produce the observed metric value at that
site. Site-specific reference metric values are generated by feeding log alkalinity, log slope
and log width into the equations previously derived from multiple linear regression (Section 3,
Table 4) for each metric. The observed metric value minus the reference metric value then
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 103
provides a measure of ecological change from reference state at the site due to that impact
gradient (e.g. SRP); the ‘impact metric’ score.
CEN draft guidelines (CEN 2004) suggest that metrics of a similar type can be grouped
together into a general impact metric, and these can then be added. Within CBASriv the
model is designed such that all the metrics explain additional species variance, and therefore
they tend to be quite separate. However, the most highly correlated metrics can be combined
by taking the largest impact metric value for that group.
DO, SRP and NH4 metrics all had correlations > 0.60 (Table 1). The NO3 metric only had a
correlation of 0.39 with SRP, however it was included to enable a single ‘nutrient’ metric to
be produced. SUBS and PH were not strongly correlated with any other metrics, and
therefore were retained as individual ‘hydromorphology’ and ‘acidity’ general impact metrics.
Therefore, to calculate TEC, the highest impact metric of DO, SRP, NO3 and NH4 (nutrient
metrics) is added to SUBS (the hydromorphological metric) and PH (the acidity metric). This
type of addition is possible since all the metrics are measured in the same units (chi2
distance x 10).
Table 1. Correlation between impact metric gradients, for the nutrient, hydromorphology and
acidity metric groups. Values are from CANOCO’s correlation matrix, although DO and PH
correlations have been multiplied by -1 to represent the metric direction (i.e. DO and SRP
metrics are positively correlated).
SUBS
DO 0.35
PH -0.10 0.04
SRP 0.26 0.61 -0.23
NO3 0.28 0.11 -0.24 0.39
NH4 0.14 0.64 0.15 0.76 0.06
SUBS DO PH SRP NO3
Determination of total ecological change (TEC) - new computer method
The concept behind this method is simply measuring the distance in ordination space
between a site-specific reference condition (predicted from the alkalinity, slope and width at
the monitoring site), and the monitoring site, as determined by the centroid of the species
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 104
that are found there (Figure 1). This distance will represent the total ecological change,
measured as chi2 dissimilarity (with bi-plot scaling).
Figure 1. Illustration of the new (computer based) method used to calculate total ecological
change. Location of the monitoring site (M) is determined from the centroid of the species
found there (e.g. nuph lut, alis pla, pota nat). The location of the reference condition is
predicted from multiple linear regression using alkalinity, slope and width (of the monitoring
site) as the predictors; predicting the position along the first three axes (e.g. prediction shown
is 0.01, 0.45 on first two axes).
Thus, the species optima along each of the first four ordination axes are taken from the
CANOCO solution file (of the CBASriv model). Since the species scores presented in the
solution file are not scaled to the eigen-value of that axis, the species score is calculated as
the eigen-value for that axis multiplied by the species optima within the model.
These species optima effectively produce ‘metrics’, although the metrics responses are
distances along each ordination axis. For each reference site and each ordination axis, the
mean optimum of the species present at the reference site is calculated. N.B. as with the
previous metrics, species scores are multiplied by 10 to produce values that are easier to
understand (chi2 distance x 10).
-1.0 1.0
-1.0
1.0
alis pla
ambl rip
brac plubrac rivcall cus
call ham
call obt
call spp
call sta
chil pol
cinc fonclad spp
cono con
dich pel
elod canfila gre
font squ
hild riv
hygr spp
lemn min
lemn pol
litt uni
marc pol
mars ema
nuph lut
pell end
pell epi
peta hyb
phal aru
pota natraco sppranu fla
ranu pel
rhyn rip
scap und
schi alpsole
spar eme
spar ere
tham alo
WIDTH
SLOPE
SUBS
DO
ALK
PH
SRP
NO3
NH4
R Md
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 105
The mean optimum of the species at the reference site along each axis, is the co-ordinates
of that reference site in ordination space (similar to the reference site scores from the
solution file). By using all the reference sites, multiple linear regression can be used to
calculate the predicted location along each axis (x1,y1,z1) given a log alkalinity, log slope and
log width value i.e. it is equivalent to the determination of impact metrics except each metric
is the positions along each axis.
The mean of the species optima (the previously derived species scores) for species present
at the monitoring site is calculated for each axis, providing the coordinates of the monitoring
site in ordination space (x2,y2,z2).
The distance between the reference condition and the monitoring site can therefore be
calculated using the distance equation:
2 212
212
212 )()()( zzyyxxd −+−+−=
Where:
d = the distance between the reference condition and the monitoring site in ordination
space
x1, y1, z1 = the coordinates of the reference conditions along the three ordination axes
x2, y2, z2 = the coordinates of the monitoring site
If biplot scaling was used this distance is chi2 dissimilarity (x10), whereas for Hill’s scaling it is
measured as Malhanobis distance. Chi2 dissimilarity (x10) is the same scale as that used in
the individual impact metrics calculation, and thus allows a comparison. Conversely
Malhanobis distance (Hill’s scaling) is equivalent to standard deviations of species turnover,
and thus provides a more useful measure of ecological change. In practise, within the
CBASriv model there is little difference. Hill’s scaling was used in the example presented.
Equation
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 106
Error estimation
Error can be estimated by constructing an ellipse around the monitoring site on the ordination
diagram, with major and minor axes determined by the error values along the first two axes
(subsequent axes have relatively little explained variance and error). Figure 1 illustrates this
method.
.
Figure 1. Calculating the error as an ellipse around the monitoring site.
Where:
d = distance between reference condition and monitoring site in ordination space
x = distance between reference condition and monitoring site along 1st ordination axis
y = distance between reference condition and monitoring site along 2nd ordination axis
θ = angle at which the direction line to the reference site bisects the error ellipse
a = error along the 1st axis
b = error along the 2nd axis
xe = error distance to bisection point along 1st axis
ye = error distance to bisection point along 2nd axis
The angle θ is calculated from simple geometry where:
yxarctan=θ
The equation of an ellipse is such that:
θθ
cossin
byax
e
e
==
Reference condition
Monitoring site
θ
a
b
y
x
e
d
xe
ye
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 107
Thus, knowing xe and ye we can then calculate the error to the edge of the ellipse, again
using the distance equation:
2 22ee yxe +=
Calculating EQR The total ecological change estimated using the method described varies from zero
(unimpacted) to no greater than 1000 (heavily impacted). Therefore, the following equation is
used to convert total ecological change in species turnover units to an EQR value:
10001000 TECEQR −
=
Where:
EQR = the ecological quality ratio required by the WFD
TEC = total ecological change as calculated in CBASriv (ranging between 0 and 1000)
4.3 Results The method of ecological distance in ordination space was initially tested with the krigging
approach to reference condition prediction (see Section 4). Since it was later decided on a
theoretical basis that distance in ordination space may not be the best method, and due to
time constraints, an illustration has not been included for the multiple interpolation method.
Figure 2 shows that there is a large difference between the decomposition method and the
distance in ordination space method. Figure 3 illustrates the improvement in error estimation
by using an ellipse rather than simple the maximum error value along any single ordination
axis. Table 1 illustrates the calculation of EQR for a selection of test sites. Figure 4 illustrates
the EQR and errors calculated using the new method, at the 20 impacted sites and 5 control
(unimpacted) sites used in (Dodkins et al. 2005a). Control site 161 is now believed to be
impacted.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 108
Figure 2. Total ecological change (TEC) calculated using the new method based on distance
between reference and monitoring site in ordination space and that estimated using the old
method of decomposition of metrics. Error bars on the new TEC values are calculated using
the ellipse method. Sites are ordered by TEC estimated using the new method.
0
100
200
300
400
500
600
700
800
900
282
136
143 5 10 23 138 57 179
206 56 161 4
285 13 182 95 102
107
180 97 98 106
103 55
EHS site reference
TEC
CB
ASr
iv (s
peci
es tu
rnov
er u
nits
x 1
000)
0
0.5
1
1.5
2
2.5
TEC
old
CB
AS
(spe
cies
turn
over
uni
ts m
inus
refe
renc
e co
nditi
on e
rror
)
TEC with CBASriv
TEC using previousCBAS anddecomposition
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 109
Figure 3. Error values calculated using the maximum error along an axis (max) and using
the ellipse method. Sites ordered as in Figure 2 and the scale is in species turnover units x
1000.
Table 1. Example of the calculation of total ecological change (TEC), Ecological Quality
Ratio (EQR) and the errors of TEC and EQR for three sites.
EHS site number 4 5 10
Axis 1 2 3 4 1 2 3 4 1 2 3 4 site centroid 0.088 0.056 0.0000.004 -0.233 0.037 -0.001 -0.003 0.000 0.000 0.0210.002
site centroid x 1000 88 56 0 4 -233 37 -1 -3 0 0 21 2
ref. condition centroid -273 76 -2 1 -408 154 18 -3 -226 -41 -17 -1
ref site error 111 49 14 21 110 48 14 22 109 47 13 21
test site - ref site 361 -20 2 3 175 -117 -19 0 226 41 38 3
distance2 130,321 400 4 9 30,625 13,689 361 0 51,076 1,681 1,4449
sum of distance2 130,734 44,675 54,210
TEC (√sum) 362 211 233
EQR 0.638 0.789 0.767
θ (degrees) -87 -56 80
xe 111 82 107
ye -3 -32 9
error (for TEC) 111 88 108
error (for EQR) 0.111 0.082 0.080
80
85
90
95
100
105
110
115
120
12528
213
614
3 5 10 23 138 57 179
206 56 161 4
285 13 182 95 102
107
180 97 98 106
103 55
EHS site reference
erro
r va
lue
ellipse errormax error
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 110
Figure 4. The EQR at 20 previously identified impacted (grey) and 5 control (white) sites.
Site 161 was previously noted for potentially being subject to impact.
4.4 Conclusions Measuring the distance in ordination space between a monitoring site and the reference
condition (predicted through krigging) for the site appears to produce an accurate estimate of
total ecological change with low error values. The error values have been reduced further
through the use of an error ellipse. The value of total ecological change (TEC) at twenty-five
test sites corresponded approximately to expectations based on a previous assessment.
The infield method will produce less accurate, but a simple, method of combining metrics.
Results from EQR calculation using this method were not presented here as it is self-evident
that the combination of metrics is less accurate than metric decomposition.
Since the initiation of the NS-SHARE project new information regarding the use of univariate
and multivariate species optima within CCA has been discovered (personal communication,
ter Braak and Smilauer 2006). This is namely that the SPEC_ENV file from CANOCO does
not produce multivariate optima. Contrary to the description within the CANOCO manual (ter
Braak and Šmilauer 2002) that states that “it is this table that is represented by the species-
environment biplot”, the SPEC_ENV file is actually just the weighted average of the species
along each (individual) environmental gradient i.e. a univariate analysis. This does not cause
an immediate problem, since the CBAS method still outperforms other methods, but it does
lead to the reflection over whether univariate or multivariate optima are more useful.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
282
136
143 5 10 23 138 57 179
206 56 161 4
285 13 182 95 102
107
180 97 98 106
103 55
EHS site reference
EQR
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 111
For example, if alkalinity and SRP are strongly correlated in the CCA, but an unusual species
exists at low alkalinity but high SRP, the multivariate optimum (indicated within a CCA biplot)
has to be located in between it’s optimum for alkalinity and its optimum for SRP, since the
environmental gradients (represented by arrows in the biplot) would be almost parallel.
However, a univariate weighted averaging (as produced by the SPEC_ENV file) can correctly
represent a low optimum for alkalinity and a high optimum for SRP. Unfortunately, in the
case of univariate analysis, there is interference from the effect of other environmental
variables. This cannot be removed within multivariate analysis (which is effectively a
generalising procedure). The only method of removing covariance due to the effect of other
variables (without removing the signal of either of the co-varying variables) is through the use
of reference sites. Thus the basic CBAS method is appropriate, although suggesting that the
optima are multivariate is not true (this will be explained further in a later report).
Thus, this distance based EQR measurement and the method of error ellipse calculation is a
useful method of measuring distance in multivariate space, however, it is likely that
ecological distance measured in multivariate space may be less accurate than the previous
decomposition method (Dodkins et al. 2005a), which combines univariate gradients, but
removes correlation between metrics by examining the correlation between their underlying
gradients. The previous decomposition method will therefore be retained for use at the
computer, whilst there will still be a quick and less accurate ‘in field’ method.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 112
Preliminary Macrophyte Survey Method (Rivers) 1st March 2005
Introduction As agreed within the NS Share macrophyte group, river sampling will follow the MTR
methodology (Holmes et al. 1999), as well as being in accord with the CEN guidance
standard for the surveying of aquatic macrophytes in running waters (CEN 2003b). However,
MTR will not be the method used for measuring ecological quality. Therefore, since surveys
and data collection should be tailored to fulfil the information needs of the project (Bartram
and Ballance 1996), there may also be additional considerations within the surveying.
Although the CBAS method looks promising for use in Ecoregion 17, other methods have not
been rejected. Field surveying will focus on determining species cover, since species
changes are believed to be the most sensitive method of measuring impacts in most cases
(Gray 1989, Angermeier and Karr 1994), and since the methods under consideration for
rivers are mostly based on species cover.
The method described here is not the final method adopted, which is still under development,
but instead illustrates some of the reasoning behind the development of the method.
Considerations prior to survey 1. The aquatic species to be recorded Different types of aquatic river macrophyte surveys result in different numbers of species
being recorded. For example RIVCON surveys in the UK list 2,332 species, PLANTPACS
lists 929 species and in MTR surveys only 133 species are specified for monitoring. Since
data collection should be driven by information requirements decisions need to be made on
the species that are to be monitored. Karen Rouen within the FBA ([email protected]) is
compiling an aquatic macrophyte species list; however this is a large and comprehensive list
and it may not be appropriate for all ecological assessment methods. Although species
additional to the list created for data analysis will be recorded, a minimum species list must
be created at this stage to prevent species being omitted from the surveys.
Arguments for a minimum aquatic macrophyte species list Even within experts there is argument over what is considered to be an aquatic macrophyte.
It may be considered that there is a danger of omitting rare species from a discrete species
list. It is unrealistic to assume that field workers will have the expertise to identify or even
notice rare species that they have never seen before, and may not even have been trained to
identify. A minimum species list should be determined for both training and routine surveying
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 113
to ensure sufficient quality control. If rare species are important for ecological assessment,
they should be included in this list.
It is strongly suggested that a list of species is prepared which is used to ensure that field
workers can identify ALL the species on the list before they begin operational monitoring.
Thus, it will be possible to distinguish species that are not recorded from species that are not
found. This will also improve estimations of optima for species within the minimum species
list since they will always be recorded when they occur.
Macrophyte surveys are different to invertebrate surveys because a whole stretch is
assessed and most of the information (species and abundance) is gathered in the field, with
only select specimen vouchers returned for confirmation in the laboratory. Accurate and
repeatable field sampling is important because much of the information can only be recorded
during the field survey and cannot be retained for later validation. A minimum species list
should improve field recording.
How to deal with bank species WFD Article 1(a) states that “the purpose of this directive is to establish a
framework...[which]...prevents further deterioration and protects and enhances the status of
aquatic ecosystems, with regard to their water needs, terrestrial ecosystems and wetlands
directly depending on the aquatic systems.” Therefore the banks (and wetlands) are
considered to be important within the WFD.
CEN guidance and technical considerations (see below) suggest that bank species should
be dealt with separately from channel species. Within the supporting hydromorphological
element of the WFD aspects of the bank and riparian area such as structure of the riparian
zone/structure of the lake shore, and connectivity have to be measured. Therefore, using
only channel vegetation for assessment may not be a problem if the ecological effects on the
banks receive appropriate weight within the hydromorphological assessment.
If bank species and channel species are combined within a single ordination, the ordination
determines the underlying gradients, but these gradients are likely to be different for channel
species (mainly determined by water quality gradients) and bank species (mainly
hydromorphology gradients). Separating bank and channel species enables gradients within
each group to be better represented.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 114
Non-causal associations between gradients and indicator species
Species that have no causal link to environmental characteristics or impacts in the main river
channel are likely to cause misleading interpretations within models that depend on species-
environment correlations. For example, Agrostis stolonifera may occur as a result of adjacent
farming land-use rather than any relationship with river water quality. Although, in statistical
analyses, it will be correlated with poor water quality, at a site where farming is well managed
to prevent pollution the species will still occur and may indicate ecological impact where
there is none. Inaccuracies created by non-causal links in the data are particularly troubling
since a model will appear to produce better estimates of ecological status overall, but certain
sites may be completely miss-assigned to a status class.
Agrostis, Epilobium and Filipendula are species which may be associated with water quality,
but are likely to be causally related to the adjacent land-use rather than the environmental
characteristics of the river. Some species, such as Juncus effusus, may be causally related
with water quality (e.g. pH) when it is found in the channel, whereas on the bank it may be
most strongly related to land use.
Using separate ordinations of bank and channel species is likely to result in stronger causal
links between the species and the underlying metrics since non-causal features (such as
water chemistry with the bank species ordination) can be omitted from the model.
Non-causal associations will produce misleading interpretation of ecological quality ratios
(EQRs) within CBAS, LEAFPACS and in Artificial Intelligence. Within LEAFPACS the
calibration of new species is based on associations with species already with a metric score,
and so the trophic rank score may be assigned by association rather than by any relationship
with trophic state. Within AI, such as a Bayesian Belief Network, probabilities of an impact
being present if certain species are present are calculated, but this will distort the EQR at
some sites if the species association with the impact is non-causal. Species which have
strong non-causal links with the underlying gradients may have to removed from the
assessment method to prevent these errors. Also, both the number of sites that have
erroneous EQRS, and the degree to which the EQR is erroneous should be assessed during
method testing.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 115
Development of the minimum species list The minimum species list was created for channel and bank species separately, although
there is overlap between the two since some species can occur both on the bank and in the
channel. Channel species were determined as species that require at least some submersion
in water during the year, whereas bank species must be able to exist on the bank without
continuous submersion by water.
Species without causal links with the underlying gradients within the assessment method
were also removed.
Options for the analysis of the separated bank species
1. conduct another CBAS assessment (although the ordination model is likely to be
quite different).
2. develop metric scores for hydromorphological aspects using the bank species.
3. ignore the bank species and allow the hydromorphological assessment to determine
potential ecological impacts on the banks.
If option one or two is taken, combining the scores from bank and channel assessments can
be undertaken. It is suggested that scores should not necessarily be weighted by the
numbers of species within the channel and bank assessments, since the banks are likely to
have more species, but not be as important in indicating ecological change within the river
corridor.
Rules for accepting a channel species in the minimum list 1. The species must require at least some submersion by the river water and thus have
some relationship water quality. Species that only require damp soils are not included.
2. The species must have some causal relationship with the gradients that form the
ordination model.
Procedure used for selecting the species list 1. Nigel Holmes’ MTR and MFR species lists, the 1998 EHS macrophyte monitoring results,
and the RIVTYPE species data, were consolidated to produce a complete species list.
2. Algae were removed if they were considered to have no diagnostic ability according to
MTR or MFR (and may be removed completely at a later date if they unduly replicate aspects
of the phytobenthos survey). Any algal monitoring must be very simple and of direct
relevance to macrophyte abundance.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 116
3. For some species, which may not easily be distinguishable, the species were combined
(as well as being listed separately). A separate optimum and niche breadth will be
determined for a species combination based on the combination of the two species within the
data. This will help retain information where field identification is difficult, though it should not
become a substitute for poor identification skills since the optima is less accurate.
4. Species that are only associated with banks, and cannot withstand moderate levels of
immersion, were removed from the channel species list.
5. Channel species that are suspected of having a non-causal relationship with water quality,
and thus introduce excessive bias in the monitoring method, were removed from the channel
species list.
6. Bank species that do not provide information to the assessment method were omitted,
since recording them will only waste time and money in the field and in data processing.
The species list Additional species to those listed below can be recorded during the field survey prior to
developing the ecological assessment method. However, this is a minimum species list and
species that are not recorded from this list are considered to be absent.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 117
CHANNEL SPECIES (139 species, 143 including species
combinations)
Batrachospermum Glyceria maxima Blue-green algal scum Glyceria plicata Charaphyte Groenlandia densa Cladophora agg. Hippurus vulgaris Filamentous green algae Hydrocharis morsus-ranae Hildenbrandia rivularis Hydrocotyle vulgaris Lemanea fluviatilis Hydrodictyon reticulatum Thick Diatom scum Hygrohypnum luridum Vaucheria spp. Hygrohypnum ochraceum Acorus calamus Iris pseudacorus Alisma lanceolatum Isoetes Alisma plantago-aquatica Juncus bulbosus Amblystegium fluviatile Jungermannia atrovirens Amblystegium riparium Lemna gibba Apium inundatum Lemna minor Apium nodiflorum Lemna minuta Azolla filiculoides Lemna polyrhiza Baldellia ranunculoides Lemna trisulca Berula erecta Littorella uniflora Blindia acuta Lobelia dortmanna Brachythecium plumosum Lotus uliginosum Brachythecium rivulare Menyanthes trifoliata Brachythecium rutabulum Myriophyllum alterniflorum Bulboschoenus maritima Myriophyllum spicatum Butomus umbellatus Nuphar lutea Calliergon cuspidatum Nymphaea alba Callitriche hamulata Nymphoides peltata Callitriche obtusangula Oenanthe crocata Callitriche obtusangula/stagnalis/platycarpa Oenanthe fluviatilis Callitriche platycarpa Persicaria amphibia Callitriche stagnalis Phalaris arundinacea Caltha palustris Phragmites australis Carex acuta Polygonum amphibium Carex acutiformis Polygonum hydropiper Carex riparia Potamogeton acutifolius Carex rostrata Potamogeton alpinus Carex vesicaria Potamogeton berchtoldii Catabrosa aquatica Potamogeton crispus Ceratophyllum demersum Potamogeton filiformis Chiloscyphus polyanthos Potamogeton friesii Eleocharis palustris Potamogeton gramineus Eleogiton fluitans Potamogeton lanceolatus Elodea canadensis Potamogeton lucens Elodea nuttallii Potamogeton natans Enteromorpha spp. Potamogeton nodosus Equisetum fluviatile Potamogeton obtusifolius Equisetum palustre Potamogeton pectinatus Fontinalis antipyretica Potamogeton perfoliatus Fontinalis squamosa Potamogeton polygonifolius Glyceria fluitans Potamogeton praelongus
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 118
CHANNEL SPECIES (Cont.) Potamogeton pusillus
Potamogeton salicifolius
Potamogeton spp. (unidentified broad leaved)
Potamogeton spp. (unidentified fine leaved)
Potamogeton trichoides
Potamogeton zizii
Potentilla palustris
Ran. pen. penicillatus
Ran. pen. pseudofluitans
Ran. pen. vertumnus
Ranunculus aquatilis
Ranunculus circinatus
Ranunculus flammula
Ranunculus fluitans
Ranunculus hederaceus
Ranunculus omiophyllus
Ranunculus peltatus
Ranunculus penicillatus
Ranunculus sceleratus
Ranunculus trichophyllus
Rhynchostegium riparioides
Riccardia
Riccia
Rorippa nasturtium-aquaticum
Rumex hydrolapathum
Sagittaria sagittifolia
Scapania undulata
Schistidium alpicola
Schoenoplectus lacustris
Scirpus fluitans
Scirpus maritimus
Sium latifolium
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 119
BANK SPECIES (131 species, 132 including species
combinations) Hildenbrandia rivularis Glyceria fluitans
Vaucheria spp. Glyceria maxima
Acorus calamus Glyceria plicata
Alisma lanceolatum Heracleum mantegazzianum
Alisma plantago-aquatica Hippurus vulgaris
Amblystegium fluviatile Hydrocharis morsus-ranae
Amblystegium riparium Hydrocotyle vulgaris
Angelica sylvestris Hydrodictyon reticulatum
Apium inundatum Hygrohypnum luridum
Apium nodiflorum Hygrohypnum ochraceum
Baldellia ranunculoides Hyocomium armoricum
Berula erecta Hypericium pteractorum
Bidens tripartita Impatiens glandulifera
Blindia acuta Iris pseudacorus
Brachythecium plumosum Juncus acutifolia
Brachythecium rivulare Juncus articulatus
Brachythecium rutabulum Juncus bulbosus
Bryum alpina Juncus effusus
Bryum pallustre Juncus inflexus
Bryum pollens Jungermannia atrovirens
Bryum pseudotriquetrum Lemanea fluviatilis
Bulboschoenus maritima Lotus pediculatus
Butomus umbellatus Lunularia cruciata
Calliergon cuspidatum Lychnis flos-cuculi
Caltha palustris Lycopus europaeus
Carex acuta Lysimachia vulgaris
Carex acutiformis Lythrum salicaria
Carex riparia Marchantia polymorpha
Carex rostrata Marsupella emarginata
Carex vesicaria Mentha aquatica
Catabrosa aquatica Mimulus guttatus
Chiloscyphus polyanthos Mnium hornum
Cicuta virosa Mnium punctatum
Cinclidotus fontinaloides Montia fontana
Conocephalum conicum Myosotis scorpioides
Dichodontium flavescens Nardia compressa
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 120
BANK SPECIES (Cont.)
Dichodontium pellucidum Oenanthe crocata
Dicranella palustris Oenanthe fluviatilis
Eleocharis palustris Orthotrichum rivulare
Eleogiton fluitans Pellia endiviifolia
Equisetum arvense Pellia epiphylla
Equisetum fluviatile Persicaria amphibia
Equisetum palustre Petasites hybridus
Eupatorium cannibinum Phalaris arundinacea
Filipendula ulmaria Philonotis fontana
Fissidens spp. Phragmites australis
Galium palustre Plagiomnium rostratum
Geum rivulare Plagiomnium undulatum
Polygonum amphibium
Polygonum cuspidatum
Polygonum hydropiper
Polytrichum commune
Potentilla erecta
Potentilla palustris
Racomitrium aciculare
Rhynchostegium riparioides
Riccardia
Riccia
Rorippa amphibia
Rorippa nasturtium-aquaticum
Rumex hydrolapathum
Sagittaria sagittifolia
Scapania undulata
Schistidium alpicola
Schoenoplectus lacustris
Scirpus fluitans
Scirpus maritimus
Scrophularia aquatica
Senecio aquaticus
Sium latifolium
Sparganium (undecided)
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 121
Field Survey Procedure This field survey procedure follows the CEN guidance standard for the surveying of aquatic
macrophytes in running waters, and is based on the MTR survey method.
A representative belt transect of 100m length within a river will be surveyed. Physical
features such as bridges, weirs etc will be avoided. Species cover will be estimated by
wading upstream in a zig-zag manner. Width and length of the survey reach will be recorded
as well as the cover of macrophytes in m2. This is better than measuring percentages or
using cover bands, and is faster to do in the field. It allows low cover of macrophytes to be
more accurately estimated, which is especially important in large rivers where species cover
would all tend to fall into the lowest band, even though there are large differences in absolute
cover.
Within the analysis of the field data it is recommended that a measure of percentage ‘bare
water’ is calculated. This is because methods such as CCA utilise relative species cover,
which can result in a loss of important information e.g. if there is very dense plant growth in
general. The inclusion of a ‘bare water’ category as a ‘species’ effectively produces an
absolute abundance analysis.
From the MTR the following definitions apply:
Channel species: macrophytes attached to a substrate that is likely to be submerged for
more than 85% of the year.
Bank species: macrophytes submerged for more than 50% but less than 85% of the time.
Bank species should also be recorded as m2. It is suggested that these are later converted to
percentage of the channel because the bank structure should relate to the channel area
since banks are regularly altered and difficult to delineate and the bank area inundated by
the river will relate to the river size. Note that this may result in bank species percentages
adding up to more than 100%.
Physical measurements will also be taken at the sites (as listed in the field survey sheets).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 122
Ecological Quality Status Bands and Errors May 2006
1. Introduction The European Water Framework Directive (WFD) (Council of the European Communities
2000) requires EQR values to be converted into status categories and the level of confidence
in these categories to be presented i.e.
Member States must aim to achieve good status in surface water bodies by 2015 and they
should also ensure that a water-body’s status does not deteriorate (Kallis and Butler 2001).
Certain surface water bodies may be designated as heavily modified water bodies (HMWBs)
or artificial water bodies (AWBs). Such water bodies are required to achieve good ecological
potential (WFD, Article 4).
2. Deciding on boundaries Since MS must aim to achieve good status, the definition of the good/moderate boundary is
important. Other boundaries are important since deterioration in status also incurs penalties.
The normative definitions within the WFD state that there may be clear ecological changes
between status classes, e.g. for phytobenthos in lakes (Table 1.2.2) Moderate Status may be
indicated by “The phytobenthic community ...displaced by bacterial tufts and coats...”.
However, current evidence suggests that, although ecological change may not always be
gradual, discrete changes due to a specific level of anthropogenic pressure are not evident
within a waterbody type. For example, ordinations of macrophytes show patchiness, but no
distinct ecological groups (Dodkins et al. 2005a), and the variability of metric values within
Annex V 1.3 “Estimates of the level of confidence and precision of the results
provided by the monitoring programmes shall be given in the plan”.
Annex II 1.3 iv. “The [reference] network shall contain a sufficient number of sites
of high status to provide a sufficient level of confidence about the value for the
reference conditions...”
Annex II 1.3 v. “The methods... shall provide a sufficient level of confidence about
the values for reference conditions to ensure that the conditions so derived are
consistent and valid for each surface water body type.”
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 123
nutrient concentration ranges was found to be very high in Danish lakes and metric values
found to show gradual rather than stepwise change (Søndergaard et al., 2005).
Since legal procedures may be required to enforce the attainment of good status or to
prevent deterioration of status, it is essential to be able to statistically support the choice of
status boundaries. The lessons learnt from biological monitoring with metrics with the US
EPA (Adler 1995) are that the legal basis for biocriteria and their implementation have been
challenged due to reliability, scientific repeatability and I inadequate cause and effect linkage.
Unlike the use of the US EPA bioassessment methods, the final EQR in the WFD has legally
binding implications.
3. Factors influencing the location of the good/moderate boundary There are four main considerations when determining the good/moderate status boundary.
1. A useable system
If good status is too stringent Member States may have use Article 4.5 of the WFD, allowing
less stringent standards if either i. economic needs served by polluting activity cannot be
achieved by other means or ii. impacts could not reasonably have been avoided, as it would
be not be economically viable to rehabilitate them. This would be costly, both through the
bureaucracy required to justify this for large numbers of water bodies, as well as wasteful of
the resources invested in developing reference site networks and water body typologies.
2. Normative definitions in the WFD
The general normative definitions of different status classes within the WFD (Annex V, Table
1.2, p. 39) are as follows.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 124
High Status
The taxonomic composition corresponds totally or nearly totally to undisturbed conditions.
There are no detectable changes in the average macrophytic abundance.
Good
There are slight changes in the composition and abundance of macrophytic taxa compared
to the type-specific communities. Such changes do not indicate any accelerated growth of
plant life resulting in undesirable disturbances to the balance of organisms present in the
water body or to the physico-chemical quality of the water or sediment.
Moderate
The composition of macrophytic taxa differs moderately from the type-specific community
and is significantly more distorted than at good status. Moderate changes in the average
macrophytic abundance are evident.
Poor/bad
Waters achieving a status below moderate shall be classified as poor or bad.
3. Concordance with other elements
When the ecological status is high, the biological and supporting hydromorphological and
physico-chemical elements status should also be high. For other status classes, the
indications of status from the different biological elements should agree unless an impact has
a greater effect on one element than the others. Potentially, inter-element scaling of status
boundaries may be required if one of the elements consistently and unjustly suggests a
failure to achieve good status without support from the other elements. The
hydromorphological and physico-chemical properties that are associated with the ecological
quality status have yet to be defined, but they may influence where status boundaries should
be placed if there are discrete rather than continuous changes in these supporting elements.
4. Concordance with other MS
Member States within the same GIG (Geographical Intercalibration Group) are likely to have
different levels of impact in their water bodies, and therefore the inter-calibration exercise is
likely to result in an adjustment of status boundaries. Flexibility in the placement of status
boundaries along the EQR range is therefore important.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 125
5. Confidence intervals
A status class should be broad enough that there is sufficient confidence in the assignment
of a site to that status class.
4. Confidence intervals as a basis for status boundaries Consideration to the factors affecting the choice of status boundaries can be determined
through judgement, broader scientific comparisons with other Member States or through later
political decisions. However, currently we are only able to conduct a scientific approach to
status boundary setting. A thorough study on determination of error and confidence requires
intensive fieldwork and further investigation beyond the scope of this study. However
preliminary analysis (see Validation report) allows initial setting of base status boundaries on
estimated confidence intervals.
The choice of status boundaries is inseparable from the determination of confidence in the
estimates of EQI, which is also required in the WFD (Annex V, 1.3), since moderate status is
defined as being “...significantly more disturbed than under conditions of good status” (WFD,
Annex V, Table 1.2). One would assume that if there are five status classes then there
should be confidence in the assignment of monitoring sites to each of these classes.
Prairie (1996) developed a relationship between the number of classes that can be
distinguished using linear regression with different r2 values (i.e. in a regression of EQR of
metric value against the value of the property that represents the real underlying impact
gradient), assuming that the frequency of observations is normally distributed and with a 95%
confidence interval for each class, as:
2131.1
rNumberclasses
−≈
Where:
Numberclasses = the number of classes that can be distinguished
r2 = the r2 value from the regression analysis
Unfortunately r2 values of metrics underlying impact gradients are limited in their utility since
a decreased r2 value can result from biological monitoring detecting impacts that were
missed by chemical monitoring. Also, correlation is not causation i.e. a metric which results
from a response to alkalinity would still have a high correlation with SRP due to the
correlation between alkalinity and SRP in the environment. However, in the absence of site-
(Equation 1)
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 126
by-site testing or controlled experiments the r2 value and confidence intervals are expected to
provide good estimates of confidence in the different status classes.
4.1 Confidence in High and Good Status Table 1 is adapted from the validation report, showing the confidence intervals calculated
and the recommended error values generated from a procedure which correctly identifies the
same percentage of impacted and unimpacted sites (estimated from physico-chemical
assessment).
Table 1. Summary Table of CBASriv performance. Range, error and confidence values of
metrics and TEC are in 1/10 th SD of species turnover units. TEC=Total Ecological Change
(estimated species change overall). EQR = Ecological Quality Ratio.
Impact metrics Ecological change
SRP NO3 NH4 SUBS DO PH TEC EQR
Underlying gradient r2 0.504 0.290 0.493 0.301 0.275 0.514 - -
Maximum value 14.0 9.4 9.7 17.5 12.5 10.9 18.7 1.0
95% confidence
interval 5.4 4.2 4.7 5.7 4.7 4.5 6.8 0.34
Max. no. of significant
categories 1.94 1.79 2.02 1.75 1.81 2.89 1.38 1.47
Recommended error
value 3.0 2.7 2.0 2.3 2.3 1.8 7.91 0.40
Minimum % detection
of impacts 78 72 87 67 32 ? - -
Due to the poor performance of physico-chemistry in representing overall status it is likely
that the recommended error values are over-estimated. The recommended error value for
TEC and EQR is determined from the maximum TEC (and minimum EQR) at reference
status. Thus, a high status boundary range of 0.6 to 1.0 is required to cover all reference
sites.
Although “if the biological quality element values relevant to good, moderate, poor or bad
status are achieved, then by definition the condition of the hydromorphological quality
elements must be consistent with that achievement and would not affect the classification of
ecological status/potential”, “hydromorphological and physico-chemical elements can be
used to distinguish between high and good status” (Council of the European Communities
2005). Thus a site designated as good status from the biology cannot be promoted to high
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 127
status from the hydromorphology and physico-chemistry, but the hydromorphology and
physico-chemistry can be used to down-grade a high biological status site to good status.
Since achievement of high status is dependent on the achievement of biological high status,
it would seem that high status and good status should be significantly distinguishable.
However Table 1.2 of the WFD states that good biological status will “deviate only slightly
from... undisturbed [reference] conditions”. Slightly cannot be construed as a significant
change, and therefore the good/moderate categories should not be significantly different. We
can conclude that high/good status can effectively be treated as one class.
Since the errors derived from validation are likely to be over-estimated, it is suggested that
high status occupies EQR 0.8-1.0, and good status occupies 0.6-0.8. Thus if a site differs
significantly from the best reference site it will fall into moderate status. If it differs slightly
from reference state it is likely to fall into the good status, although it potentially could be in
the high status as well (though this could be further differentiated by hydromorphology and
physico-chemistry). This also means all the reference sites are contained within the
high/good status class (which is statistically indistinguishable).
With current estimates we can only say for certain that a site below the worst reference site
(EQR = 0.6) plus the 95 % confidence interval (0.34) = EQR of 0.26, significantly deviates
from reference state. This is the presumed minimum legal statistically distinguishable
deviation. However, in most cases court action against polluters could still be justified at
higher EQR since
i. the reference condition relevant to the monitoring site is unlikely to be the worst reference
site within the reference network
ii. reference sites need to be further reviewed and may show improvements in light of this
and
iii. once the monitoring network is defined much more precise reference conditions can be
developed individually for each site using additional historical information.
Manipulating the EQR calculation
If having a reference site with an EQR of 0.6 is unpalatable the EQR scale can be
manipulated by using different values in the EQR calculation. For example this equation:
1521 dTEC
EQR−
=
Produces a minimum EQR at reference state at 0.87 (TEC = 7.9). Within the 520 site data
set, the most impacted site has a TEC of 18.7, resulting in an EQR of 0.15. However,
changing the EQR distribution in this manner produces reference sites which score above an
EQR of 1.0. This effect can easily be ignored by treating such exceedences as 1, but a
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 128
worse problem is the effect on the confidence interval. For example, the 95 % mean
confidence interval is a TEC of 6.8. Thus within the new measure the EQR confidence
interval = 6.8/15 = ± 0.45. For a 90 % confidence interval it is ± 0.38. The denominator can
be increased to reduce the confidence interval e.g. a numerator of 28 and denominator of 25
gives a worst reference site EQR at 0.804 and worse impacted site at 0.372. This puts all
reference sites above an EQR of 0.8 and the confidence interval at 95 % drops to ± 0.27
(0.23 at 90 % confidence). However, most impacted site then get placed at an EQR of 0.37.
Non-linear transformations will have a non-linear effect on the confidence interval. Thus, it is
recommended that the high/good status band previously described and the calculation of
EQR recommended in the Validation report is currently retained.
4.2 Confidence in Moderate to Poor Status Error is likely to be different at different EQR values (Clarke 2000) as well as within different
river types. However the available data are not suitable for assessing these errors. It could
be considered that the 95 % confidence interval for EQR (± 0.34), or indeed the 90 %
confidence interval (± 0.29) could be used to set the rest of the boundaries. However, these
confidence intervals are either side of a sample, and therefore a boundary would have to be
0.34 x 2 = 0.68 EQR units wide; i.e. there is insufficient space for even a single additional
confidence interval beyond good status.
Table 2 shows the r2 values required to produce a certain number of significant status
boundaries using the equation of (Prairie 1996) (Equation 1). An r2 of 0.76 (two distinct
classes) is unlikely to be obtainable using any metric, and the r2 of 0.97 required for 5
significantly distinct status classes seems impossible. Thus, realistically, we can only
determine one confidence interval, which must be the significant difference from reference
state. However, we must remember that r2 values represented within the validation exercise
are likely to underestimate the reliability of biological impact predictions since the physico-
chemical values against which they are assessed are highly variable. When consistency in
the biological metrics can be more fully assessed, and if site-specific reference condition
predictions are improved, it may be possible to provide much smaller error values, and
consequently able to distinguish more quality classes.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 129
Table 2. r2 values required in linear regression to achieve distinction between a certain
number of classes with 95 % confidence for normal and uniform distribution of observations.
Minimum required r2 value Number of
classes Normal Uniform
1 - -
2 0.76 0.90
3 0.90 0.96
4 0.94 0.98
5 0.97 0.98
Moderate status is defined as “significantly more disturbed than under conditions of good
status” (Table 1.2, WFD). Since moderate and good status are adjacent and therefore sites
in the top of the moderate status class can never be significantly different from sites at the
bottom of the good status class, this is clearly non-sensical. However, as described in the
previous section, considering high/good status as an indistinguishable class, moderate status
can be considered as being significantly different to the reference state.
To be classified as poor status in the WFD, a water body must “achieve a status below
moderate”. There is no clear distinction between poor and bad status. Since there is no
suggestion of significance in these statements it is suggested that the moderate/poor and
poor/bad classes are evenly divided in order to spread the lack of confidence between them
and thus spread the risk of Type I error (an apparent drop in status, which isn’t real).
The resulting status boundary table is shown in Table 3 within the conclusions.
5. Future Work to Improve Confidence Estimates Determining error is extremely complex and includes the following elements:
1. Surveyor error
2. Spatial and temporal natural variation
3. Errors in prediction of reference condition
4. Error associated with insufficient information (e.g. low numbers of species at a site)
Surveyor error can be reduced through quality control (including using a minimum species
list) whilst spatial and temporal error can be reduced though using the correct scale of
sampling. Errors associated with insufficient information appeared to be consistent, although
large (Figure 1).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 130
0
0.5
1
1.5
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
number of species
devi
atio
n (s
peci
es tu
rnov
er u
nits
)mean max
Figure 1. The variation of mean and maximum deviation of the CBAS SRP metric from a
predicted metric score. The predicted score is based on a linear regression of the metric
score against soluble reactive phosphate concentration with number of species at a site.
The authors believe the largest source of error is in a mismatch between actual reference
condition of a monitoring site and reference condition prediction (which is limited by the
number and type of predictive variables that can be used). Although MLR within CBASriv has
improved predictive ability, mismatch error could be reduced further i.e. it is much more
accurate to determine biological state-change for a site than to determine deviation from a
modelled reference condition. Thus, once the monitoring network is established the MLR
equations can be used to provide suggested reference conditions, but these should be
further examined in light of historical information, catchment information and expert
interpretation at each individual site to provide the best estimate of reference conditions on a
site by site basis. Once there is confidence in the determination of reference condition at a
particular monitoring site (which may take several years of surveying and observation and
reference metric adjustment), the assessment of deviation from this state should be
accurate. CBASriv provides metrics relating to different impact gradients, and therefore it is
simple to adjust reference metrics specific to an individual monitoring site through additional
historical information or professional advice. We should also remember that a change of
state (which can be much more accurately determined) is as important as a drop from good
to moderate status.
Future work on examining the variation in metrics and EQR at individual sites (within different
river types and at different EQR), whilst ensuring there is no change in impact pressure could
also help to produce estimates of metric consistency. These may be better estimates of error
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 131
than regression against physico-chemical parameters, which appear to be unreliable
estimators of impact.
6. Other Member States Work on Status Boundaries or Error Estimates Other Member State’s attempts to derive status boundaries and confidences are in their
infancy, and most contain serious flaws in logic or application.
LEAFPACS (Nigel Willby, currently unpublished)
LEAFPACS uses a criterion that the good/moderate boundary should be where the number
of reference species is equal to the number of impact species. However, there is no reason
to believe that the point at which the number of reference and impact species is the same is
the point at which impacts become significant. The binary nature of the species scores
employed in this method (impact or reference species) is used to reduce natural variation,
but this results in treating all reference species (or impact species) as directly equivalent to
each other in terms of their ability to indicate high status (or impact), regardless of whether
they would actually occupy slightly different positions along an impact gradient.
STAR
The presentation of confidence as percentage probability of belonging to each status class
has been suggested. However, these distribution curves can be flat, with a higher probability
of a site belonging to another class than any single class e.g. if the probability of belonging to
high status is 25%, even if this is the highest value, it is still more probable that the site
belongs to a different class (75% chance). Also, if probability distributions are designed to
add up to 100% they do not give a true estimate of confidence i.e. a metric that is poor at
predicting the true EQR could still produce a high percentage prediction for a particular
status class because it is most likely to be in that class. However, in reality, the confidence in
the prediction for all classes could be extremely poor. An automatic assumption of a
unimodal curve in the probability distribution of status class is also erroneous. For example, if
a metric is based on species diversity, a low number of macrophyte species could indicate
either strong toxic effects or low nutrient conditions (a bimodal probability distribution).
EMCAR
Veronique Adrianenssens and Julian Ellis have investigated risk of misclassification in the
EMCAR project. The standard deviation (SD) of the EQR value is determined by sampling a
site several times. This is completed for sites with different EQR values within the water-body
type. A graph of SD against EQR is then established for the water body type. At a particular
EQR it is then assumed that the error is normally distributed around this value. Then a
percentage probability distribution can be determined based on this distribution.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 132
Unfortunately sampling error reconstructed from the sites at different EQR will be mostly due
to differences between the sites (rather than between the EQR values). The error graph is
also created such that it is assumed that the error at an EQR of 0 or 1 is zero, which is false.
The assumption of a normal distributed probability distribution curve (the confidence classes)
is also false. This method has all the associated problems of using probability distribution
curves (previously mentioned).
STARBUGS (Centre for Ecology and Hydrology 2005)
STARBUGS was developed as part of the European STAR project and “uses estimates of
the biological sampling (or survey) variation and other estimation errors to simulate and
quantify the uncertainty in assessments of ecological status”. Thus it guides the user in
assessing which errors are relevant and in combining these errors, but does not directly help
in the accurate determination of these errors.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 133
7. Conclusions Since ecological status is a subjective concept, the confidence associated with the estimate
of EQR can only be in relation to a subjective measure. r2 correlations of EQR and metrics
with underlying gradients suggest that any metric system won’t be able to attain more than
two significantly different status classes. However, variability in physico-chemical monitoring
means that, in practice, correlations with the underlying gradient are likely to over-estimate
the error in biological monitoring i.e. physico-chemistry is probably less reliable as an
estimate of impact than biology. Future studies over time within different river types and at
different levels of impact may help to determine consistency within metrics and thus produce
more accurate error values.
Table 3 shows the suggested status boundaries based on a high/good class that contains all
the reference sites, and is the width of a confidence interval. An attempt to spread error
within the remaining status classes (moderate to poor) results in an even spacing of EQR
between them. For legal purposes, we have 95 % confidence that an EQR of 0.26 or less
significantly deviates from reference status.
It is expected that much higher confidence in the metrics and EQR can be obtained in the
future when:
i. reference conditions are tailored specifically for each monitoring site
ii. field survey methods are tailored to the specific method of ecological assessment to
reduce natural variation, and
iii. when addition studies assessing internal consistency are carried out.
It is recommended that, until inter-calibration has been undertaken and assessment methods
have been formally accepted and tested, estimates of the errors of EQR values and status
classes and of status boundaries remain simple and flexible. We should also be aware of
future developments, particularly the EMCAR project.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 134
Table 3. Suggested status boundaries, which spread error evenly throughout the EQR scale
and ensures a statistically reliable determination of the good/moderate boundary.
Status EQR range
High > 0.8 - 1.0
Good >0.6 - 0.8
Moderate > 0.4 - 0.6
Poor > 0.2 - 0.4
Bad ≤0.2
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 135
Validation of CBAS for Rivers 1 Introduction It is difficult to validate methods of measuring ecological status for the purposes of the Water
Framework Directive (WFD) since ecological quality is defined in only very basic qualitative
terms for each biological element. Thus different Member States are likely to assess different
structural and functional components of the biology of water bodies and will, therefore,
produce ecological status classes that may be incompatible.
Measures of ecological change are often assessed by comparison with a gradient of
chemical change, e.g. (Dawson et al. 1999). CCA, which forms the basis of the CBAS model,
uses linear regression to adjust the site scores in an iterative procedure in order to achieve
the maximum separation in the niches of the species. Thus species optima (which are a
weighted average of the site scores) have a linear relationship with the underlying
environmental gradient, although the scaling of species optima uses Hill’s scaling and thus
the optima values reflect ecological distance (standard deviations of species turnover). To
differentiate between the CBAS model created for rivers, and that for lakes, the river CBAS
model is referred to as CBASriv, and the lake CBAS model as CBASlak.
For validation purposes, it is useful to consider that metrics are a reconstruction of an
underlying environmental gradient, commonly an impact gradient. Thus, accuracy of this
reconstruction can be determined through linear regression of the metric value against a
value that represents the underlying gradient. This will overestimate the error associated with
the metric, since it is believed that the biological characteristics can integrate impacts over
time and thus detect changes that chemical monitoring may miss. However, such validation
gives an indication of the minimum performance of the metrics and of CBASriv.
CBASriv metrics do not replicate environmental monitoring. Species optima are derived from
between 12 and 24 chemistry samples at 520 sites. The metric scores are not subject to the
large intra-annual variance associated with chemical spot samples, but instead provide an
estimate of average species response to the impact pressure. Thus species optima integrate
the temporal variation in chemistry as well as representing impact as biological change.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 136
2 Methods 2.1 Validation of the impact gradients Is the CBASriv model realistic?
A comparison of an unconstrained ordination (DCA) and the constrained ordination (CCA)
was undertaken to ensure that the main gradients in the species variance have been
identified. Ordinations were conducted using CANOCO version 4.5 (ter Braak and Smilauer
2002).
Examining MTR and combined gradients within the CBASriv model
Any scoring system can be incorporated into the CBASriv model to examine how it performs
relative to the CBASriv metrics. The metric score for each site is calculated and the results
added to the model as a supplementary ‘environmental’ variable (i.e. doesn’t affect the
model). This process was undertaken for the MTR score (Holmes et al. 1999). The resultant
gradient should produce species optima in the same way the revised MTR method derived
by Nigel Willby (the LEAFPACS method) does. Within this report, this metric is referred to as
MTR (LEAFPACS). Correlations between the variables that explain species variance (the
weighted correlation matrix from CANOCO) were also determined, as well as Pearson’s
correlation coefficients calculated for the relationship between the metric score at sites and
the value of the underlying environmental variable.
Mean Flow Ranking (MFR) scores have been derived by Nigel Holmes (Environment Agency
2002) to represent flow variation at river sites and could potentially be another expert based
impact metric. Therefore, this score was also added as a supplementary variable to the
CBASriv model to examine the utility of this metric.
High correlations were previously found between NO3, NH4 and SRP gradients within the
CBASriv model and this suggests that a single combined gradient of these variables would
explain more species variance and simplify the CBASriv model. A combined ‘EUTRO’
gradient was produced by conducting a CCA using only NO3, NH4 and SRP environmental
variables. CANOCO produces site scores along the first four ordination axes within the
solution file. These axes are linear combinations of the environmental gradients and so the
EUTRO gradient can be formed by adding the four axis scores at each site (weighted by the
eigen value of each axis) to produce a single site score.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 137
2.2 Regressions of metrics against the underlying impact gradient Internal validation by linear regression
Metric values derived from the 520 site CBASriv model were linearly regressed against their
underlying environmental gradient to obtain r2 values. This was completed for models that
used the original CBAS method of weighting by abundance and indicator value, as well as by
omitting the indicator value and omitting both indicator value and abundance weighting
(simply the centroid of the species optima). 95% and 90% confidence intervals were
determined for each metric value. Due to the small difference between unweighted and
weighted CBAS metric scores, all subsequent analyses were conducted on unweighted
CBAS metrics scores (just the mean of species optima).
Site scores from the EUTRO and MTR gradients were regressed against probably the most
relevant underlying impact gradient, soluble reactive phosphate (SRP). The species optima
in the MTR (LEAFPACS) metric are not identical to MTR scores but are weighted averages
of these scores. As a comparison, the traditional MTR scores for each site were regressed
against SRP.
External validation by linear regression
To enable a fair comparison between MTR and CBASriv a regression must be completed
which uses sites that were not to create the CBASriv model. 50 % of the 520 CBASriv sites
were removed at random for this validation and a new CBASriv model was created with the
remaining sites.
The residuals of the linear regression of the SRP metric vs. chemical SRP concentration
were also plotted to examine if there was a bias (i.e. more extractable pattern). Residuals
were plotted against the predicted metric values (Racca and Prairie 2004).
2.3 Regressions of reference condition adjusted metrics against the underlying impact gradient There may be correlation between the metric value and the property that represents the
underlying impact gradient (e.g. SRP concentration) and this may in part be due to
correlations of the metric value with a natural gradient (e.g. alkalinity). Thus, a metric that
responds to alkalinity but not to SRP will still produce a good correlation with SRP if alkalinity
and SRP are strongly correlated in the environment. The only way to remove the effect of
correlations between the metric and non-impact gradients (e.g. alkalinity) is to use reference
conditions. These reference condition-adjusted metric values are called ‘impact metric’
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 138
values since the difference between reference and monitoring site metric values represents
the impact at that site.
The reference value for an impact variable was estimated as follows. The same variables
(alkalinity, slope, width) used to predict reference metric scores were used to predict
expected chemistry values, using multiple linear regression. For each of the 520 CBASriv
sites, and for each impact gradient (SRP, NO3, NH4, SUBS, DO, PH) the predicted
reference condition chemistry is subtracted from the actual chemical value (in the same way
in which impact metrics are produced). Since there is error within the reference site
predictions, both for metrics and for chemistry, the standard error (SE) from the multiple
linear regressions is subtracted from the scores prior to regressing reference condition
adjusted metric scores (impact metrics) against reference site adjusted chemistry values.
2.4 Regression of EQR against expected EQR values
Decomposition (Dodkins et al. 2005a) is used in CBASriv to combine impact metrics. Since
the impact metrics are all to the same scale (standard deviations of species turnover) they
can be combined to produce a value of total ecological change (TEC). There are correlations
between the metrics, and therefore direct addition would over-estimate TEC. However, the
CBASriv CCA model produces correlations between the underlying gradients and the four
orthogonal (uncorrelated) environmental axes. Therefore each metric can be multiplied by it’s
correlation with axis1. The resultant values are the correlated contributions of the metrics to
axis 1, and therefore the highest value from the metrics represents the total contribution to
axis 1. This is repeated for the first four axes, and the highest metric scores along each axis
can be directly added to produce TEC (since the axes are orthogonal).
Since some correlation coefficients are negative values, and all the impact metrics have
been converted to positive values, the correlation coefficients were also made positive.
Negative maximum values along an axis indicate that the site is of higher quality than
predicted by the reference conditions, and these sites are ignored. Eigen values of the axes
do not have to be used for weighting the metric values, since the species variance explained
by a metric is already incorporated through the use of Hill’s scaling. This time, standard
errors of reference site prediction were not incorporated prior to decomposition, as it is felt
inappropriate to combine scores with error estimates.
Values representing difference in chemistry from reference conditions cannot be added,
since they are all to different units i.e. it makes no sense to add pH units to log mean
substrate diameter. However from the linear regressions between each biological metric and
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 139
the underlying impact gradient we know the change in SD of species turnover caused for
each unit of environmental change. Thus we can convert chemistry and physical values to a
‘Hill’s scaling equivalent’.
TEC (from decomposed metrics) was plotted against the expected TEC using decomposed
physico-chemical values. Finally, to determine the accuracy of the field method, the EQR
from the field method was regressed against the EQR calculated using the decomposition
method.
3. Results 3.1. Assessment of the metric gradients
Is the CBASriv model realistic?
Figure 1 and 2 show a CCA and DCA of the results used to develop the CBASriv model. In
both ordinations, the variance explained and direction of the gradient are similar for the
environmental variables. The species are also located at very similar relative positions within
each ordination.
Examining MTR and combined gradients within the CBASriv model
Figure 3 and 4 show the CBASriv model CCA ordination with MTR, MFR and the combined
eutrophication gradient (EUTRO) as supplementary variables. MTR, MFR and EUTRO
gradients explain the largest amount of species variance within the model (Table 1) and this
suggests that MTR, MFR and EUTRO are excellent metrics. Further investigation shows that
the expert metrics (MTR and MFR) are highly correlated with the first and, to some extent,
the second ordination axis, but have low correlations with subsequent axes (Table 2). MTR
and MFR are also highly correlated with each other (Table 2). These are correlations in
explaining species variance, however MTR (traditional and LEAFPACS) is also correlated
with alkalinity when Pearson’s correlation coefficients are calculated directly between the
MTR and alkalinity values at the sites (Table 3).
The correlations suggest that expert scores may really only reflect the main underlying
macrophyte gradient (i.e. axis 1) and not specifically an impact gradient. Indeed, Table 2
shows that MTR is more correlated with alkalinity (0.589) than with SRP (0.579). The
EUTRO gradient performs better than MTR in that it is more correlated with SRP (0.904)
than alkalinity (0.562). EUTRO is also more correlated with NO3 and NH4 than MTR is. The
CBASriv SRP metric is modelled exactly along the SRP gradient (and thus has a correlation
of 1 with SRP). The Pearson’s correlation coefficients between the SRP metric and log SRP
(Table 3) is 0.771, compared to 0.662 for MTR (LEAFPACS).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 140
3.2 Regressions of metrics against the underlying impact gradient Internal validation by linear regression
Table 4 illustrates that CBAS performs slightly worse without the use of abundance or
indicator weighting for SRP, SUBS, DO, WIDTH and SLOPE gradients, but with PH, NH4,
NO3 and ALK gradients CBAS performs better. The use of weighting has little effect
suggesting that abandoning indicator values and abundance measures is worth the reduction
in effort during fieldwork. For example, the difference between an r2 of 0.596 and 0.607 (SRP
metric) is only around a 1% increase in classification resolution (i.e. resolution between
status classes) (calculated from (Prairie 1996)). The combined EUTRO metric performs well,
but represents the SRP gradient less effectively than the SRP metric (Table 4). Since
ordination is a generalisation method that extracts patterns, the combined EUTRO gradient
represents the general pattern of change in NO3, NH4 and SRP as a whole and therefore
when the ratio of the variables which comprise the combined gradients are different to the
general trend, the species will not reflect the contributing gradients as effectively.
The MTR (LEAFPACS) metric performs far better than the traditional MTR score (Table 4). In
both the MTR (LEAFPACS) metric and the MTR score, the use of abundance weighting
appears to have a moderate benefit.
External validation by linear regression
Table 5 shows the performance of the CBASriv metrics against MTR (LEAFPACS) and MTR
scores when regressed against their underlying gradients. SRP, PH and NH4 metrics all
produce higher r2 values than the MTR (LEAFPACS) metric and the MTR score. The
CBASriv SRP metric has over 10% greater ability to distinguish classes than MTR
(LEAFPACS).
Figure 5 shows the CBASriv SRP metric plotted against log SRP concentration and the
residuals from this regression are shown in Figure 6. There is no pattern in the residuals and
therefore no bias in the SRP metric.
The range of metric values at the 520 sites used to develop the CBASriv model are shown in
Table 6, with 95% and 90% confidence intervals. PH, SRP, SUBS and DO metrics all have
good ability to distinguish quality classes (2 ½ quality classes or more).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 141
Table 1. Marginal (individual) variance explained by MTR, MFR and EUTRO gradients
compared to gradients within the CBAS model. All variables are significant at P = 0.0001.
Total inertia = 6.592.
Variable Eigen value (λ1)
MTR 0.370 MFR 0.347 EUTRO 0.280 SRP 0.267
ALK 0.235
SLOPE 0.218
SUBS 0.215
NH4 0.199
DO 0.198
PH 0.178
NO3 0.144
WIDTH 0.085
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 142
Figure 1. The CBASriv model CCA. Only species with weight of 3% or greater are shown to
increase clarity.
Figure 2. The DCA of species used in the CBASriv model with variables overlayed. Only
species with weight of 3% or greater are shown to increase clarity.
-1.0 1.0
-1.0
1.0
ambl flu ambl rip
apiu nod
brac plubrac riv
call obt
call spp
chil pol
cinc fon
clad spp
cono con
dich pel
elod canequi flufila gre
font ant
font squ
hild riv
hygr spp
lema
lemn min
lunu crumarc pol
myri spi
nuph lut
pell end
pell epi
peta hyb
phal aru
pota natraco spp
ranu pen
rhyn riprori nas
scap und
spar eme
spar ere
tham alo
vauc
vero bec
WIDTH
SLOPE
SUBS
DO
ALK
PH
SRP
NO3
NH4
-8 2
-15
ambl fluambl rip
apiu nod
brac plu
brac riv
call obt
call sppchil pol
cinc fon
clad spp
cono condich pel
elod canequi flu
fila gre
font ant
font squ
hild riv
hygr spp
lema
lemn min
lunu cru
marc pol
myri spi
nuph lut
pell end
pell epi
peta hyb
phal aru
pota nat
raco spp ranu pen
rhyn rip
rori nas
scap und
spar eme
spar ere
tham alo
vauc
vero bec
WIDTH
SLOPESUBSDO
ALKPH
SRP
NO3
NH4
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 143
Figure 3. The CBASriv CCA bi-plot model with MTR, MFR and a eutrophication gradient
(EUTRO) used as supplementary variables (species or sites not shown for clarity). Axes 1
and 2.
Figure 4. The CBASriv CCA bi-plot model with MTR, MFR and a eutrophication gradient
(EUTRO) used as supplementary variables (species or sites not shown for clarity). Axes 1
and 3.
-1.0 1.0
-1.0
1.0
WIDTH
SLOPE
SUBS
DO
ALK
PH
SRP
NO3
NH4
EUTRO
MTR
MFR
-1.0 1.0
-1.0
1.0
WIDTH
SLOPE
SUBS
DO
ALKPH
SRPNO3
NH4EUTRO
MTR
MFR
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 144
Table 2. Weighted correlation matrix of the supplementary variables with the environmental
axes and environmental variables used the CBASriv model.
Axis 1 1
Axis 2
-
0.048 1
Axis 3 0.011
-
0.036 1
Axis 4 0.050
-
0.047 0.093 1
WIDTH 0.331 0.157 0.239 0.140 1
SLOPE
-
0.684
-
0.177 0.107
-
0.243
-
0.426 1
SUBS 0.605 0.428
-
0.454 0.116 0.170
-
0.498 1
DO
-
0.618
-
0.182 0.183 0.449
-
0.132 0.420
-
0.351 1
ALK 0.633
-
0.512
-
0.232 0.259
-
0.035
-
0.362 0.339
-
0.313 1
PH 0.334
-
0.779
-
0.262 0.230
-
0.034
-
0.120 0.098 0.044
0.75
7 1
SRP 0.758
-
0.205 0.283
-
0.265 0.130
-
0.368 0.258
-
0.608
0.52
1
0.23
4 1
NO3 0.504 0.089 0.369 0.347
-
0.015
-
0.207 0.283
-
0.112
0.44
3
0.23
7
0.39
1 1
NH4 0.597
-
0.239 0.110
-
0.617 0.095
-
0.290 0.141
-
0.644
0.39
9
0.15
4
0.76
0
0.06
0 1
EUTRO 0.777
-
0.172 0.279
-
0.334 0.089
-
0.359 0.264
-
0.627
0.56
2
0.24
8
0.90
4
0.51
1
0.87
0 1
MTR 0.830
-
0.340 0.157 0.256 0.118
-
0.427 0.308
-
0.352
0.58
9
0.42
1
0.57
9
0.45
2
0.40
9
0.58
6 1
MFR 0.833
-
0.295
-
0.082 0.062 0.152
-
0.431 0.349
-
0.384
0.54
2
0.41
9
0.53
1
0.32
5
0.42
7
0.53
8
0.88
4 1
Axi
s 1
Axi
s 2
Axi
s 3
Axi
s 4
WID
TH
SLO
PE
SUBS
DO
ALK
PH
SRP
NO
3
NH
4
EU
TRO
MTR
MFR
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 145
Table 3. Pearson’s correlation coefficients between environmental variables and metrics.
Environmental
variables Metrics
alk. SRP NO3 mSRP mALK mNO3
MTR
(LEAFPACS) MTR
alkalinity 1
SRP 0.566 1
Env.
vars.
NO3 0.341 0.336 1
mSRP 0.648 0.771 0.385 1
mALK 0.781 0.634 0.290 0.878 1
mNO3 0.461 0.555 0.597 0.799 0.666 1
MTR(LEAFPACS) 0.668 0.662 0.401 0.930 0.924 0.825 1
Metrics
MTR 0.579 0.574 0.379 0.742 0.740 0.654 0.847 1
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 146
Table 4. r2 values of the CBAS metrics, reconstructed EUTRO and MTR metrics, and the
MTR score for a linear regression against their relevant underlying gradient CLEARER. MTR
(LEAFPACS) metric is derived from reconstructed species optima along the MTR gradient
and weighted by indicator values.
r2 value
Metric
Impact gradient
regressed against
CBASriv
Without
indicator
value
Without
indicator
value and
only pres/abs
SRP log SRP 0.607 0.600 0.596
PH pH 0.565 0.564 0.565
NH4 log ammonia 0.480 0.502 0.497
SUBS mean substrate diameter
(phi)
0.471
0.460 0.443
DO 2√ % dissolved oxygen 0.435 0.428 0.418
NO3 log nitrate 0.335 0.325 0.357
WIDTH log width 0.258 0.256 0.256
ALK log alkalinity 0.617 0.616 0.618
SLOPE log channel slope 0.447 0.437 0.420
EUTRO log SRP 0.594 0.588 0.588
EUTRO EUTRO gradient 0.598 0.594 0.585
MTR (LEAFPACS) log SRP 0.455 0.454 0.440
MTR score log SRP n/a 0.323 0.309
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 147
Table 5. r2 values from a regression of CBASriv metrics against underlying gradients
(external validation). No indicator score or abundance weighting was used for the CBASriv
metrics but abundance weighting was used for the MTR score and the MTR (LEAFPACS)
metric (since they perform better with it).
Metric r2 value
SRP 0.504
PH 0.514
SUBS 0.301
NH4 0.493
DO 0.275
NO3 0.290
WIDTH 0.225
ALK 0.542
SLOPE 0.355
MTR (LEAFPACS) 0.454
MTR score 0.309
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 148
y = xR2 = 0.000000002
-1.5
-1
-0.5
0
0.5
1
1.5
-1.5 -0.5 0.5 1.5
Predicted values (SRP metric)
Res
idua
ls
Figure 5. Regression of the SRP metric at the 520 CBASriv sites against log SRP, showing
90% (inner) and 95% (outer) confidence intervals for an individual site i.e. ± 4.9 and ± 5.8
respectively.
Figure 6. Residuals of the linear regression in Figure 5 plotted against predicted values
(SRP metric). Line of best fit, r2 and equation of the line of best fit are shown.
-20
-15
-10
-5
0
5
10
15
20
-3 -2.5 -2 -1.5 -1 -0.5 0
log SRP
SRP
met
ric
(SD
of s
pp. t
urno
ver
x 10
)
R2 = 0.596
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 149
Table 6. Range of metric values at 520 sites with the range of metric scores (SD of species
turnover x 10) and mean confidence limits for an individual site estimate CLEARER. The
maximum number of divisions (categories of impact) that can be significantly distinguished =
range of values/(confidence limit x 2) is also given.
Confidence limits
(for individual estimate)
Metric Range Min Max (95 %) ± (90 %) ±
Max no. divisions at
95% confidence
SRP 24 -14.00 10.20 5.82 4.88 2.5
NO3 16 -9.00 6.60 4.71 3.95 2.0
NH4 21 -13.60 7.50 5.31 4.45 2.4
SUBS 25 -7.00 17.67 5.97 5.01 2.5
DO 22 -11.40 10.80 5.21 4.37 2.5
PH 28 -5.80 22.00 4.79 4.02 3.5
3.3 Regressions of reference condition adjusted metrics against the underlying impact gradient Table 7 shows the coefficients required to predict site-specific reference metric scores, using
alkalinity, slope and width at the monitoring site. Table 8 shows the coefficients used to
predict site-specific reference condition chemistry values. The r2 values for the prediction of
reference condition chemical state are low, suggesting poor ability to predict chemical
condition.
Figure 8 shows the relationship between the impact metric value above predicted reference
condition and the log SRP concentration above the predicted reference condition. The
confidence intervals are a little smaller (increased confidence) than when the non-reference
condition adjusted metric values are used (Figure 5), i.e. ± 5.4 compared to ± 5.8. Therefore,
accounting for the reference condition enables the level of impact to be distinguished more
clearly. However the improvement is not as much as may be expected, probably because of
a poor ability to predict chemistry at reference state.
Table 9 gives a summary of the impact metric performance in detecting impacts and in
correctly identifying high status sites as determined from the physico-chemical results. The
standard error of the reference condition prediction for the metrics was subtracted from the
metric score; however, this error value seemed too low since (except for pH) a high
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 150
percentage of sites with impact were correctly detected while most of the unimpacted sites
were considered to have impacts. Thus Microsoft Excel ‘Goal seek’ was used to set the
standard error for each metric such that the same percentage of impacted and unimpacted
sites were identified. The resultant performance and SEs for each metric are shown in Table
10. The reference conditions for the chemical properties were poorly predicted i.e. r2 values
for all physico-chemistry except pH do not exceed 0.175 (Table 8). This may have resulted in
the poor ability to determine the number of unimpacted sites e.g. it is suggested that there
are 393 sites without a DO impact (Table 9). Thus the new error value is likely to over
estimate the error, but may be useful initially to prevent possible unimpacted sites being
considered impacted or vice versa. The PH metric was unusual in that there was an
apparently high number of impacted sites that were not identified. When the PH metric
standard error was recalculated using ‘Goal seek’, a negative value (-1.8) was obtained,
which suggests that negative impact metric values indicate an impact. This may result from a
combination of very few real pH impacts and thus an exaggerated estimate of chemically
determined impacts (Table 9). However, an error value of 1.8 (error values are not negative)
is suggested for the PH impact metric (this value is used in Table 8), and the poor
performance is attributed to a poor ability to predict an impact with the chemical results rather
than the biology.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 151
Table 7. The coefficients of the multiple linear regression used to predict the site-specific
reference value for the six impact metrics. The results are in SD of species turnover units x
10, the same units used for species optima.
Coefficients for predictive variable
Metric
log
alkalinity
(mg/l
CaCO3)
log
slope
(m/km)
log
width
(m)
Constant
Mean Standard Error of
Prediction (±)
r2
SRP 3.872 -0.472 -11.711 0.350 0.608
NO3 0.857 -0.325 0.886 -5.437 0.460 0.082
NH4 3.332 -0.388 -0.834 -8.888 0.420 0.466
SUBS 1.165 -0.970 1.767 -6.266 0.300 0.414
DO 2.375 -0.721 -7.386 0.260 0.494
PH -8.050 15.691 0.504 0.662
Table 8. The coefficients of the multiple linear regression used to predict the site-specific
reference value for physico-chemical properties. The result is in the same units as the
environmental variable.
Coefficients for predictive variable
physico-
chemical
variable
log
alkalinity
(mg/l
CaCO3)
log
slope
(m/km)
log
width
(m)
Constant
Mean Standard Error
of Prediction (±)
r2
SRP 0.192 -0.117 -1.947 0.065 0.175
NO3 0.153 0.0864 0.0538 -0.387 0.105 0.032
NH4 0.141 -0.245 -0.468 -1.266 0.114 0.155
SUBS 1.221 -1.103 0.865 -6.605 0.631 0.168
DO -0.219 0.271 9.963 0.116 0.157
pH 0.912 5.985 - 0.709
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 152
Figure 7. Regression of the SRP impact metric (metric score above reference condition) at
the 520 CBASriv sites against log SRP concentration, showing 90% (inner) and 95% (outer)
confidence intervals for an individual site, i.e. ± 4.6 and ± 5.4 respectively.
-10
-5
0
5
10
15
20
-1 -0.5 0 0.5 1 1.5 2
log SRP (above predicted ref conditions)
SRP
impa
ct m
etri
c(S
D o
f spp
. tur
nove
r x
10)
R2 = 0.440
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 153
Table 9. Rate of impact identification based on a comparison of impact metric value (minus
SE) and impact CLEARER predicted from physico-chemistry (with predicted reference
condition and SE subtracted). The numbers of sites only add up to 517, as 3 sites did not
contain any indicator species.
Impact metric
SRP NO3 NH4 SUBS DO PH
SE from MLR of metrics 0.35 0.46 0.42 0.30 0.26 0.50
NO. SITES WITH:
impact detected (correct) 366 296 400 164 77 8
impact not detected (incorrect) 16 26 13 28 47 203
unimpacted sites identified (correct) 45 73 67 84 17 241
unimpacted site not identified (incorrect) 90 122 37 241 376 65
% sites (with impact) correctly identified 96 92 97 85 62 4
% of sites (without impact) correctly identified 33 37 64 26 4 79
IMPACT METRIC VALUES:
range 21 15 19 20 17 26
min -6.9 -5.5 -8.9 -2.7 -4.1 -14.7
max 14.0 9.4 9.7 17.5 12.5 10.9
mean 95% confid 5.4 4.2 4.7 5.7 4.7 4.5
mean 90% confid 4.6 3.5 3.9 4.8 4.0 3.8
max no. categories at 95% 1.92 1.77 1.99 1.78 1.74 2.82
r2 0.440 0.364 0.411 0.300 0.263 0.261
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 154
Table 10. Impact identification rate using a higher impact metric SE (determined such that %
impacted and unimpacted sites correctly identified are equal).*pH is an exception - see main
text. The statistics on impact metric values are the same as in Table 9.
Impact metric
SRP NO3 NH4 SUBS DO *PH
Recalculated SE 3.01 2.69 2.00 2.27 2.32 1.81
NO. SITES WITH:
impact detected (correct) 297 233 358 129 40 4
impact not detected (incorrect) 85 89 55 63 84 207
unimpacted sites identified (correct) 105 141 89 217 126 275
unimpacted site not identified (incorrect) 30 54 15 108 267 31
% sites (with impact) correctly identified 78 72 87 67 32 2 % of sites (without impact) correctly identified 78 72 86 67 32 90
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 155
3.4 Regression of EQR against expected EQR values Figure 8 shows the frequency distribution of total ecological change (TEC) values within the
520 site data set. This is TEC determined by decomposition. Figure 9 shows the predicted
TEC distribution from decomposition of the physico-chemistry (using Hill’s scaling equivalent
units). It is interesting to notice that the range of values is much greater for the biologically
derived TEC. This is likely to be due to the effectiveness of iterative weighted averaging
procedure (i.e. Reciprocal Averaging) within the CBASriv model that uses co-occurrences of
species at sites to maximise the separation of species’ niches.
Figure 10 shows the TEC estimated from physico-chemistry (in Hill’s scaling equivalents)
against the TEC determined from the biological impact metrics. The relationship is weak,
although this is probably more to do with the difficulty of predicting reference condition and
impact with chemical properties, than a failure of the metrics to determine impacts. This poor
ability to predict TEC from chemistry, and thus to use chemistry to validate the biological
metrics results, means that the use of the confidence interval (or chemical TEC against
metric TEC) as an error value is inappropriate. Thus the error value for TEC was determined
as the maximum TEC value for any reference sites (7.9), i.e. error is ± 7.9 (SDs of species
turnover).
The TEC from the decomposed impact metrics (TECd) was converted to EQR using the
maximum TEC value (18.7, see Table 11) such that the EQR range is 0 to 1, with a high
value representing high status i.e.:
2020 dTEC
EQR−
=
The TEC generated from the field method (TECf) is slightly less accurate and tends to be
over-estimated CLEARER. The maximum TEC value using the field method was 30.5. Thus
the equation for converting TECf to an EQR was:
3030 dTEC
EQR−
=
Figure 11 shows a regression of the TEC generated from decomposition against that
generated using the field method for the 520 sites. The correlation is strong, with a mean 95
% confidence interval of ± 0.0843 (i.e. 8% of the total range of the EQR).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 156
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 M
T o ta l E c o lo g ic a l C h a n g e (S D o f s p e c ie s tu r n o v e r )
Freq
uenc
y
0
10
20
30
40
50
60
70
80
90
100
8.88
58.
98.
915
8.93
8.94
58.
968.
975
8.99
9.00
59.
029.
035
9.05
9.06
59.
089.
095
9.11
9.12
59.
149.
155
9.17
9.18
59.
2M
ore
TEC estimated from physico-chemistry (Hill's scaling equivalent)
Freq
uenc
y
Figure 8. Frequency distribution of TEC values at 517 sites determined through
decomposition of the impact metrics.
Figure 9. Frequency distribution of TEC values at 517 sites determined through
decomposition of the impact metrics.
TEC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Frequency 25 50 66 50 49 50 47 38 26 35 28 21 12 9 2 5 0 1 2
TEC 8.89 8.90 8.928.93 8.95 8.968.988.999.019.029.049.059.079.08 9.109.11 9.139.149.169.17
Frequency 4 6 11 35 45 46 90 89 47 37 28 32 24 5 7 1 1 3 1 3
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 157
R2 = 0.1096
-5
0
5
10
15
20
25
8.85 8.9 8.95 9 9.05 9.1 9.15 9.2 9.25
TEC determined from physico-chemistry(Hills scaling equivalent)
TEC
(SD
of s
pp. t
urno
ver
x 10
)
Figure 10. Regression of TEC determined using the impact metrics against TEC estimated
from physico-chemical properties.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 158
Table 11. Impact identification rates. Determined from a comparison between TEC from
biological metrics against TEC estimated from physico-chemical properties.
TECd
Recalculated SE 7.91
NO. SITES WITH:
impact detected (correct) 144
impact not detected (incorrect) 373
unimpacted sites identified (correct) 0
unimpacted site not identified (incorrect) 0
% sites (with impact) correctly identified 27.853
% of sites (without impact) correctly identified -
Min 0
Max 18.7
Mean 95% confid 6.8
Mean 90% confid 5.7
Max no. categories at 95% 1.37
r2 0.110
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 159
R2 = 0.9503
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0EQR (decomposition)
EQR
(fie
ld m
etho
d)
Figure 11. EQR determined using decomposition regressed against EQR using the field
method. The 95% confidence interval for an individual site is shown (± 0.0843 EQR units).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 160
4. Discussion The similarity of the DCA and CCA ordinations for the sites used to develop the CBASriv
model suggest that the CCA ordination, which forms the basis of the CBASriv metrics, does
not omit any main environmental gradient in the species data.
The MTR (LEAFPACS) gradient explained a high amount of species variance, but the metric
value was more highly correlated with alkalinity than log SRP concentration, and therefore it
is more an indicator of alkalinity than of eutrophication. The correlation between MTR, MFR
and the first two ordination axes suggests that expert metric scores may only be able to
reflect the main gradients in species change within the environment. Therefore, if the main
change in species is also strongly correlated with natural gradients (such as alkalinity),
expert metrics may be unable to separate the natural gradients from the impact gradient.
Strong correlation between a metric and a natural gradient is not a problem if the reference
conditions are predicted effectively, and the remaining signal (following subtraction of the
reference condition) is causally related to the suggested impact. Accuracy of reference
condition prediction is therefore extremely important, and may be the main source of error in
EQR calculation.
A CBASriv metric derived from the combined nutrient impact gradients (EUTRO) explained a
high amount of species variance, but the ability to determine the source of impact was lost. It
was felt that, despite the correlations between NO3, NH4 and SRP concentrations, these
metrics should be retained as separate metrics as the metric response signature would be
more useful as a diagnostic than the EUTRO metric.
MTR (LEAFPACS) performs far better (r2 = 0.454) than the traditional MTR score (r2 = 0.309)
in a linear regression of these metric scores against log SRP. This is probably due to the
rescaling and optimum adjustment through reciprocal averaging which occurs with the
LEAFPACS (and CBAS) method. However, even with external validation, the CBASriv metric
SRP performs better (r2 = 0.504) than either MTR (LEAFPACS) or MTR. This improvement
equates to a 10% increase in the ability of the CBASriv SRP metric to distinguish status
classes than the MTR (LEAFPACS) metric. Internal validation using all 520 sites gives an r2
value of 0.596 when the CBAS SRP metric is regressed against the log SRP chemistry. The
true r2 value for the CBAS SRP metric is likely to lay somewhere between the internal and
external validation r2 values (0.504 - 0.596).
Except for pH (for which there may be no or very few impacts in Ecoregion 17), attempts to
predict physico-chemical values at reference state (and thus to estimate impacts from the
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 161
chemistry) were not very successful. The regression of reference site-adjusted ‘impact
metrics’ against the reference site-adjusted chemical concentration showed only a small
increase in prediction confidence. This small increase in the ability of metrics to distinguish
impacts when reference sites have been taken into account is therefore likely to be due to a
failure to estimate impacts with the chemical results, rather than due to poor prediction of the
biological metric reference value or poor metric performance. This analysis enabled the error
values for each metric to be determined that were reasonably robust (but are probably over-
estimated), and gave a minimum performance of impact detection. For example, 87 % of NH4
impacts, 78 % of SRP impacts, 72 % of NO3 impacts and 67 % of substrate impacts can be
detected using CBASriv. Although there is some correlation between the nutrient impacts,
the low correlation between SRP, SUBS and PH metrics suggest that nutrient,
hydromorphological and acidification impacts can be distinguished using CBAS.
Similarly, the estimation of total ecological change (TEC) using physico-chemical properties
was not very successful. However, a comparison of the distribution of TEC values derived
from the biological characteristics with that derived from chemical characteristics illustrates
the benefit of using reciprocal averaging for separating species optima along an impact
gradient. Weighted averaging with presence-absence results along an environmental
gradient (as used by CBASriv to produce the species optima) is the mean value of the
environmental variables at the sites at which the species occurs, accounting for the joint
occurrence of species at sites. This maximises the dispersion of species optima along the
impact gradient (ter Braak 1987), p. 74).
In comparison, another investigation (not detailed here), was attempted in which the
distribution curves of species (using frequencies along the impact gradient) were determined.
At a monitoring site, the distribution curves for all species along the SRP gradient were
combined to produce a total frequency distribution from which the median value was taken.
However, the species distributions all tend to occur towards the middle of the gradient, and
thus it was impossible to distinguish impacted and unimpacted sites. The use of median
optimum values are being attempted in some Member States, but it is suggested that
Reciprocal Averaging is far superior in producing niche separation and thus in optima
estimation.
Despite the problems in recreating a physico-chemical TEC gradient, the biological TEC
gradient appeared internally consistent and the field and computer method of metric
combination produced similar results. It is suggested that the method of metric
decomposition is retained for WFD reporting purposes, whilst the field method of impact
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 162
metric calculation (which is identical to the computer method) can be used for diagnostic
purposes. The validation of TEC has also allowed equations from TEC to EQR using both
the field and the decomposition method to be developed.
Despite the benefits of reciprocal averaging in the LEAFPACS method (which is equivalent to
using CBAS, but with the MTR score as the underlying gradient), there are potential
concerns with other aspects of LEAFPACS.
1. The final MTR (LEAFPACS) score is still dependent on the accuracy of the original expert
scores i.e. reciprocal averaging increases the replicability of the method, but does not
increase confidence that the score represents the impact. This study suggests that the MTR
score is more representative of an alkalinity rather than and SRP gradient.
2. Species that did not have an MTR score have been given optima using weighted
averaging. However, care needs to be taken when species are given optima where there is
little causal relationship with the impact gradient, as they may be more indicative of, for
example, landuse. This concern is applicable to all methods (including CBAS).
3. Since a discrete typology is used in LEAFPACS, the species chosen as indicator species
vary with the water body type. Therefore there are potentially strong boundary effects.
LEAFPACS is a useful method where good biological and physico-chemical data are
unavailable (e.g. Ecoregion 18). This is currently the case in many Member States, although
within several years all Member States will have sufficient data to utilise CBAS. CBAS can
also incorporate any impact gradient, including site scores from MTR.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 163
5. Conclusions The main CBASriv metric (SRP metric), when linearly regressed against the SRP
concentration, has an r2 value of 0.504 with external validation and 0.596 with internal
validation. The MTR (LEAFPACS) approach being adopted for Ecoregion 18 has a lower r2
value of 0.454. It is also suggested that more of the correlation between MTR (LEAFPACS)
and log SRP is due to a correlation with alkalinity, than is the case with the CBASriv SRP
metric.
The main CBASriv metric (SRP metric) is better at reflecting the underlying impact gradient
than MTR (LEAFPACS). The addition of other diagnostic gradients derived from a
multivariate ordination, and the use of interpolated reference conditions, produces a method
for measuring ecological status that is transparent, allows impact diagnosis, and is likely to
outperform other metrics based on species lists.
The poor predictive ability of physico-chemistry at reference condition has made it difficult to
effectively validate the biological metrics. However this shows that biologically derived
metrics are more consistent than chemical assessment. Since sufficient long-term data are
not available to accurately estimate metric consistency, errors have been estimated based
on the ability to predict impacts determined through physico-chemistry. Thus the error values
shown in Table 12 are likely to be overestimated and the CBAS performance (Table 12) is
likely to be underestimated. This investigation has highlighted the benefits of reciprocal
averaging for the estimation of species optima and suggests that accurate determination of
reference condition may be the most important feature of a good EQR assessment method.
It is suggested that site specific reference conditions generated from MLR could be further
improved by using historical or expert information specific to the monitoring site.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 164
Table 12. Summary table of the performance of CBASriv. Maximum, error and confidence
values of the metrics and total ecological change (TEC) are given in 1/10th SD of species
turnover units.(*) The r2 value refers to the regression of the metric value against the value of
the property that represents the underlying gradient and does not accounting for reference
conditions. The reference condition r2 value is a good indicator of how well the reference
condition metric value is predicted for that metric. Maximum values give an indication of the
importance of the underlying gradient in it’s affect on macrophyte ecology. Minimum %
detection of impacts is not given for PH, TEC and EQR since the physico-chemical
assessment meant that values provided in the validation were not likely to be representative.
Impact metrics Ecological change
SRP NO3 NH4 SUBS DO PH TEC EQR
*underlying gradient r2 0.504 0.290 0.493 0.301 0.275 0.514 - -
Reference condition r2 0.608 0.082 0.466 0.414 0.494 0.662 - -
Maximum value 14.0 9.4 9.7 17.5 12.5 10.9 18.7 1.0
95% confidence interval 5.4 4.2 4.7 5.7 4.7 4.5 6.8 0.34
Recommended error value 3.0 2.7 2.0 2.3 2.3 1.8 7.91 0.40
Minimum % detection
of impacts 78 72 87 67 32 ? - -
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 165
LAKES
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 166
Preliminary Macrophyte Survey Method (Lakes) Introduction Data collection should always be guided by information needs (Bartram and Ballance 1996).
Whilst assessment of eutrophication within lakes has a good history of research e.g. (Arts
2002, Murphy 2002), measuring ecological quality is a relatively new concept.
It is idealistic to believe that the extent of impact can be determined from a direct comparison
between a lake macrophyte inventory and an inventory from one of the 13 lake reference
conditions (Table 2). Variation due to sampling, natural variation within lakes types, and
temporal variation necessitates a focus on specific impacts or macrophyte responses to
enable an impact signal to be distinguished from this variation. Similarity measures could be
used, or metrics that relate to underlying impact gradients. With CBAS (Dodkins et al. 2005a)
natural variation is reduced by using a similarity measure that is defined by change along
underlying impact gradients.
The major anthropogenic impact within Ecoregion 17 lakes is Eutrophication, mainly due to
increases in nitrogen and phosphorous. Total phosphorous (TP) tends to be most linked with
primary productivity within lakes, and although not all TP is bioavailable (Peters 1981), it is
currently the best surrogate for bioavailable phosphorous (Peters 1986). The physical
structure of the lake will also be ecologically important. However, separating change due to
natural and artificial physical properties is probably best done through hydromorphological
assessment (weighted according to the effect of the change on ecology) for many impact
types. If the physical conditions as they exist within the monitoring lake can be used to
ensure similar transects are compared it may allow more sensitivity with detecting other
impacts.
Certain hydromorphological impacts, e.g. cumulative effects of water regulation, are not
immediately apparent through routine hydromorphological survey. A study on water
regulation in lochs in Scotland showed complete eradication of littoral macrophytes in lochs
with high weekly or annual water level fluctuations and major community responses with
increases in shore slope (Smith et al. 1987). The same study states that the flora in the
littoral zone of lakes is largely dependent on the degree of exposure to wave action and the
shore substrate.
CBAS has been found to be highly effective with rivers since the weighted averaging of
species optima reduces inter-annual temporal variation (e.g. due to floods) which can result
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 167
in some species being omitted from the survey. However, within lakes, natural hydrological
variation is much lower and the lack of macrophyte species in lakes could actually provide
valuable information on anthropogenic impacts, such as water regulation (Smith et al. 1987).
This suggests that CBAS alone may not be sufficient to characterise all the main
components of ecological change. CBAS is still likely to be better than other measures of
compositional change (such as Chara extinction), but it does not take account of species
diversity changes. Thus additional in-field measures could be recommended which could be
used to develop supplementary metrics.
This lake survey methodology has been developed using the draft CEN guidance for the
surveying of macrophytes in lakes (CEN 2003a), with reference to (Environmental Protection
Agency 2002, McElarney 2002). The methodology detailed here was used to survey lakes
with a gradient of impact, providing data for the NS-SHARE project. For the current Lake
Survey Methodology suggested for the WFD, see the NS-SHARE Methods Manual (III)
Lakes.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 168
Equipment and Physiochemical monitoring
Equipment Safety and navigation equipment:
1. boat
2. Self-inflating life-jacket (per person)
3. thigh boots
4. 1:50,000 maps of lake and surrounding area, preferably laminated
5. Hand-held Geographical Positioning System
6. first-aid kit
7. sanitised wipes
8. binoculars
9. mobile phone
For surveying:
1. double-sided rake grapnel with sufficient rope (preferably hemp)
2. bathyscope (can be made by gluing a Petri-dish on the end of a black pipe of
dimensions 8.3 cm external diameter x 1m length)
3. sinkable measuring tape with concrete weight or lead weighted line (to mark
survey transects).
4. record sheets
5. sharp pencils and eraser
6. hand lens
7. copy of survey procedure
8. polarising sunglasses
9. records from any previous macrophyte surveys
10. camera with polarising lens
11. echo-sounder
12. pH, DO (%) and temperature meter.
For sample collection and examination:
1. Large white tray
2. dry newspaper
3. 30 ml sample bottles (for algae)
4. plastic bags for samples
5. macrophyte identification books and keys
6. marker pens
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 169
Environmental measurements These measurements should be available for each lake or transect, although the data may
have been obtained from different surveys. If in doubt about the availability of supporting
environmental data, additional samples should be collected.
Physical and chemical measurements required as supporting data:
1. total nitrogen
2. total phosphorous
3. alkalinity
4. pH
5. dissolved oxygen at surface (*)
6. altitude
7. lake area
8. mean lake depth
9. transect slope (*)
10. transect substrate (*)
11. Optical Density at 340 nm
12. solid and drift geology (from CEN guidance)
(*) Indicates that this has not been measured during phytobenthos surveys, and therefore
must be measured.
Survey Procedure Prior to survey: Establish permission for access and determine how access will be achieved practically.
Summary: Boat surveys of the macrophytes are undertaken at 4-6 transects, perpendicular to the shore,
ensuring all habitat types are represented. A shore survey, 10 m either side of each transect,
is also undertaken. The DAFOR scale is used to record abundance over each transect.
Survey should ideally be done twice, once in May to June, and next in July to September.
Similar lake types should be sampled at the same time of year. Survey should follow periods
of low rainfall, when clarity is maximised and lake levels are near normal. The main purpose
is to survey true aquatics, although it is recommended that helophytes and amphibious
species are also recorded separately.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 170
Procedure:
1. Circumnavigate the lake by boat or foot, familiarising yourself with the different habitats.
Select 4-6 transects representative of the lake morphological habitats (habitats listed on field
sheet). The number of transects should be judged based on lake size and habitat variability.
The main objective of the survey is to record ALL the species present within the lake.
Location of transects should also cover areas where landuse could be negatively affecting
water quality.
2. Note the GPS location of the first transect at the shoreline. Record physical conditions
listed on the field sheet.
3. Transect survey: Move gently away from the shore in a perpendicular direction, dropping
the double-headed rake grapnel at the following intervals (also marked on transect survey
sheet), and dragging it for 1 m.
0, 2.5, 5.0, 7.5, 10, 25, 50, 75, 100 (m) from the shoreline
After 10m distance from shore can be measured with GPS. For GARMIN GPS: At transect
position 0, press ‘MARK’ and save in GPS memory. Press ‘GOTO’ and select transect
position 0. The GPS will tell you the distance from this at 10m intervals.
a) Measure the depth with echo sounder at each location.
b) At each sampling point, view the lake bed with the bathyscope for macrophytes
(otherwise the grapnel will disturb the sediment and viewing will be difficult) and
record their presence
c) Throw the grapnel four times parallel to shore at each location.
d) Tick off species on record sheet (presence) at each location.
e) Using a combination of the bathyscope and the grapnel samples taken, estimate the
cover of each species on the DAFOR scale (Table 1) for the whole transect and note
on the survey sheet. The aim is to get an approximate measure of macrophyte
abundance, so species that are not being pulled up by the grapnel, but can be
observed, should still be adequately reflected within the DAFOR score recorded.
f) Note both the depth and distance from shore at the zone of extinction (where no more
macrophyte growth is evident). [Depth can be around 3m for peaty lakes, 6 m for
limestone lakes]
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 171
Keep any samples that need later identification in plastic bags labelled with i. lake number ii.
date iii. transect number iv. species number. Filamentous algae and Chara can be placed in
labelled 30 ml sample tubes.
Table 1. DAFOR scale. Equivalent percentage is estimated based on personal judgement.
Scale Abundance
Descriptor
(Equivalent
Percentage)
1 Rare <1
2 Occasional 1 - 2
3 Frequent 2 - 10
4 Abundant 10 - 40
5 Dominant > 40
NB. Grapnel sampling should not be used where rare and/or legally protected species are
known to be present.
4. Physiochemical measurements: At the end of the transect, with the hand-help meter,
record:
• surface DO (%)
• pH
• temperature
IMPORTANT: Additional variables need to be recorded if the site is not coincident with the
phytobenthos survey. A 100 ml water sample can be taken in a plastic bottle for this purpose
[BRIAN is OD340 measured in the lab, is this a big enough sample to get all the appropriate
analyses done (TP, TN, alk) and is a plastic bottle suitable?]
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 172
5. Shore-line survey: Wade 10 m either side of the transect, within the shallow littoral zone.
Circle the DAFOR scale on the survey sheet. If species are washed up in the strandline, but
not growing at the transect, record these separately (as they may not be growing there).
Growth form of the species should also be recorded, irrespective of the growth form
elsewhere. Use the symbols as follows:
submerged - a ‘v’ pointing to the bottom (v)
emergent - a ‘v’ pointing upwards (^)
floating leaved - a line, representing the water surface (-) bank or shore - a line representing the bank’s slope ( \ )
(free-floating plants are evident from the species, although (~) can be used if desired.
6. Repeat the process for each transect. One survey sheet is provided for each transect.
Ensure at the end of each transect, and the end of the lake survey, that all relevant
information is included on the sheet. Especially check:
• lake number
• transect number
• surface DO
Any additional species seen which are outside the transect should also be noted (write in
notes section of field sheet), though this should rarely occur if the transects have been
adequately chosen. Note name, growth form, DAFOR (within the whole lake) and
shore/water.
Back in the lab: Confirmation by an independent national/regional expert should be sought for species that
cannot be identified. Species that were difficult to identify, need to be sent to an expert, or
could be later questioned e.g. Callitriche, Ranunculus and fine leaved Potamogeton spp.,
should be stored. Bryophytes can be dried in paper, large leaved higher plants can be dried
in paper and pressed, and fine leaved species should be stored in 70% ethyl alcohol or
industrial metholated spirit (although this will not keep them indefinitely, and colour may be
lost from the flowers and foliage).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 173
Field sheet Field sheet design should enable the data to be input easily into a spreadsheet in the format
in which it is to be used, with a minimum of mistakes, and therefore the same format should
be followed as far as possible. For this reason a species list is used, and the species are
ordered in columns going down the page. All these species within the species list should be
identifiable by the surveyor prior to survey to ensure that the absence of the species means
that the species is not present, rather than not identified. Additional species should also be
noted. The field sheet should be printed double sided such that one sheet is equivalent to a
single transect and shoreline survey.
Data Usage
It is important to understand the final use of the data in case of an inability to collect all the
data specified or if additional judgements need to be made.
The data collected may be treated as individual transects, although compatibility with current
data may require combination into a single lake sample (e.g. combination of macrophyte data
and averages of chemistry data). Transects will be segregated by the lake typology (see
Table 2) and probably also by slope and underlying substrate. As well as physiochemical
data, transect depth, DO, zone of extinction (depth and distance from shore) and transect
slope (calculated from depths along the transect) will be used. Macrophyte data analysis is
likely to utilise only the true aquatics, although alternative metrics may be generated for
marginal plants, especially if they respond to water level changes. The macrophyte data
should represent the species present within the whole lake. Abundance measures are less
important than determining presence, although it is useful to know if there is a very large or
very small amount of a species (using the DAFOR scale).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 174
Table 2. (Draft) Lake Typology for Ecoregion 17 (Environmental Protection Agency 2005a).
Alkalinity
mg/l CaCO3
mean depth
(max depth)
m
lake
area
(ha) Lake
Type <20 20-100 >100 <4(12) >4(12)<50>50
1
2
3
4
5
6
7
8
9
10
11
12
13 some lakes > 300 m altitude
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 175
TRANSECT SURVEY 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
depth (m)dist. from shore (m) 0 2.
5 5 7.5 10 25 50 75 100 0 2.5 5 7.5 10 25 50 75 100
Apium inundatum Pot. gramineus Batrachospermum Pot. lucens Callitriche brutia Pot. natansCallitriche hamulata Pot. obtusifoliusCallitriche hermaphrodita Pot. pectinatusCallitriche obtusangula Pot. perfoliatusCallitriche platycarpa Pot. polygonifoliusCallitriche stagnalis Pot. praelongusCeratophyllum demersum Pot. pusillusCeratophyllum submersum Pot. salicifoliusChara sp. Pot. x lintoniiCrassula helmsii Pot. x nitensElatine hexandra Pot. x ziziiElatine hydropiper Ranunculus aquatilisEleocharis acicularis Ranunculus baudotiiEleogiton fluitans Ranunculus circinatusElodea callitrichoides Ranunculus peltatusElodea canadensis Ranunculus penicillatusElodea nuttallii Ruppia cirrhosaEriocaulon septangulare Ruppia maritimaGroenlandia dena Ruppia sppHippuris vulgaris Sagittaria sagittifoliaHydrocharis morsus-ranae Sparganium angustif.Isoetes echinospora Sparganium emersumIsoetes lacustris Sparganium minimumJuncus bulbosus Sparganium natanslagarosiphon major Stratiotes aloidesLemna gibba Subularia aquaticaLemna minor Utricularia intermediaLemna polyrrhiza Utricularia minorLemna trisulca Utricularia sp.Litorella uniflora Utricularia vulgarisLobelia dortmanna Zannichellia palustrisMyriophyllum alterniflorum Zostera marinaMyriophyllum aquaticumMyriophyllum spicatum Filamentous algaeMyriophyllum verticillatum Fontinalis antipyretica Najas flexilis Fontinalis squamosaNitella sp. Sphagnum spp.Nuphar lutea fucoids/seaweedsNymphea albaNymphoides peltataPolygonum amphibiumPot. alpinusPot. berchtoldiiPot. coloratusPot. crispusPot. filiformisPot. friesii
DA
FOR
DA
FOR
Lake no. IGR Surface DO (%)Lake name Date temp (°C)
Transect no. Surveyor pHadverse weather conditions? Y / N
Location type Substrate Neighbouring landuse (within 15m)inlet clay gravel/pebble broadleaved woodland rough grasslandoutlet peat cobble coniferous woodland improved grasslandembayment earth boulder scrub and shrubs grazed grasslandexposed area silt bedrock wetland tilled landisland sand moorland rock/screeother (list) urban/suburban
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 176
SHORE SURVEY
circle correct abundance
Acorus calamus D A F O R Lysimachia vulgaris D A F O R Amblystegium fluviatile D A F O R
Alisma lanceolatum D A F O R Lythrum salicaria D A F O R Amblystegium riparium D A F O R
Alisma plantago-aquatica D A F O R Mentha aquatica D A F O R Blindia acuta D A F O R
Angelica sylvestris D A F O R Mimulus guttatus D A F O R Brachythecium plumosum D A F O R
Apium inundatum D A F O R Montia fontana D A F O R Brachythecium rivulare D A F O R
Apium nodiflorum D A F O R Myosotis scorpioides D A F O R Brachythecium rutabulum D A F O R
Baldellia ranunculoides D A F O R Oenanthe crocata D A F O R Bryum alpina D A F O R
Berula erecta D A F O R Oenanthe fluviatilis D A F O R Bryum pallustre D A F O R
Bidens cernua D A F O R Persicaria amphibia D A F O R Bryum pollens D A F O R
Bidens tripartita D A F O R Petasites hybridus D A F O R Bryum pseudotriquetrum D A F O R
Bulboschoenus maritima D A F O R Phalaris arundinacea D A F O R Calliergon cuspidatum D A F O R
Butomus umbellatus D A F O R Phragmites australis D A F O R Cinclidotus fontinaloides D A F O R
Caltha palustris D A F O R Rorippa amphibia D A F O R Conocephalum conicum D A F O R
Carex acuta D A F O R Rorippa nast.-aquat. D A F O R Dichodontium flavescens D A F O R
Carex acutiformis D A F O R Rumex hydrolapathum D A F O R Dichodontium pellucidum D A F O R
Carex riparia D A F O R Sagittaria sagittifolia D A F O R Dicranella palustris D A F O R
Carex rostrata D A F O R Schoenoplectus lacustris D A F O R Jungermannia atrovirens D A F O R
Carex vesicaria D A F O R Scirpus fluitans D A F O R Lunularia cruciata D A F O R
Catabrosa aquatica D A F O R Scirpus maritimus D A F O R Marchantia polymorpha D A F O R
Chiloscyphus polyanthos D A F O R Scrophularia aquatica D A F O R Marsupella emarginata D A F O R
Cicuta virosa D A F O R Senecio aquaticus D A F O R Mnium hornum D A F O R
Eleocharis palustris D A F O R Sium latifolium D A F O R Mnium punctatum D A F O R
Eleogiton fluitans D A F O R Sparganium emersum D A F O R Nardia compressa D A F O R
Equisetum arvense D A F O R Sparganium erectum D A F O R Orthotrichum rivulare D A F O R
Equisetum fluviatile D A F O R Stachys palustris D A F O R Pellia endiviifolia D A F O R
Equisetum palustre D A F O R Thamnobryum alopec. D A F O R Pellia epiphylla D A F O R
Eupatorium cannibinum D A F O R Typha angustifolia D A F O R Philonotis fontana D A F O R
Filipendula ulmaria D A F O R Typha latifolia D A F O R Plagiomnium rostratum D A F O R
Fissidens spp. D A F O R Valeriana D A F O R Plagiomnium undulatum D A F O R
Galium palustre D A F O R Veronica anagallis-aqua. D A F O R Polytrichum commune D A F O R
Geum rivulare D A F O R Veronica beccabunga D A F O R Racomitrium aciculare D A F O R
Polygonum amphibium D A F O R Veronica catenata D A F O R Rhynchostegium ripa. D A F O R
Polygonum cuspidatum D A F O R Veronica scutellata D A F O R Riccardia D A F O R
Polygonum hydropiper D A F O R Viola palustris D A F O R Riccia D A F O R
Potentilla erecta D A F O R D A F O R Scapania undulata D A F O R
Potentilla palustris D A F O R D A F O R Schistidium alpicola D A F O R
Glyceria fluitans D A F O R D A F O R Sphagnum spp. D A F O R
Glyceria maxima D A F O R D A F O R Hildenbrandia rivularis D A F O R
Glyceria plicata D A F O R D A F O R Vaucheria spp. D A F O R
Heracleum mantegazz. D A F O R D A F O R D A F O R
Hippurus vulgaris D A F O R D A F O R D A F O R
Hydrocharis morsus-ran. D A F O R D A F O R D A F O R
Hydrocotyle vulgaris D A F O R D A F O R D A F O R
Hydrodictyon reticulatum D A F O R
Hygrohypnum luridum D A F O R Notes:Hygrohypnum ochraceum D A F O R
Hyocomium armoricum D A F O R
Hypericium pteractorum D A F O R
Impatiens glandulifera D A F O R
Iris pseudacorus D A F O R
Juncus acutifolia D A F O R
Juncus articulatus D A F O R
Juncus bulbosus D A F O R
Juncus effusus D A F O R
Juncus inflexus D A F O R
Lemanea fluviatilis D A F O R
Lotus pediculatus D A F O R
Lychnis flos-cuculi D A F O R
Lycopus europaeus D A F O R
grow
th fo
rm
grow
th fo
rm
grow
th fo
rm
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 177
Development of CBAS for Lakes 1. Introduction The same methodology has been used to assess ecological status for lakes as was used in
rivers. To prevent repetition this chapter has been kept concise with frequent reference to the
river CBAS method. To distinguish the river and lake models, CBAS for rivers is referred to
as CBASriv, and CBAS for lakes as CBASlak.
2. Combining Lake Survey Data The data from a total of 619 lakes was used to create the CBASlak model. The data was
collated from the following sources:
1. 472 lakes from the Northern Ireland Lake Survey (NILS) that had associated physico-
chemical data including an estimate of maximum water depths. Macrophytes were surveyed
between 1988 and 1990.
2. 147 lakes surveyed in the Republic of Ireland under an EPA ERTDI funded project (Free
et al., 2006) between from 2001 to 2003 and including potential reference lakes. Where there
were replicate sites within lakes recorded separately the mean chemistry and macrophyte
abundance values were used. Since the macrophytes are likely to be responding to spring
rather than summer chemistry data, only the spring chemistry data was retained.
Pseudoreplication was avoided by not including lakes in the CBAS model that had already
been surveyed in the above combined dataset i.e. data from 20 lakes was available from
(McElarney 2002) (replicates of NILS sites) and 33 Lakes from the NSSHARE survey
conducted by Dodkins in 2005 (replicates of NILS sites).
Combining macrophyte data The ERTDI macrophyte data was expressed as weight collected on the first rake and
therefore needed to be converted to an equivalent of the DAFOR scale to allow combination
with the NILS data. This conversion was carried out by assuming that the distribution of
abundance values would be the same as the distribution of values using the DAFOR scale
within the NILS data. Therefore log transformed rake weights from Free and Littles were
categorised into 5 classes, dividing each class at the 20th percentile within the data. The
categorised ERTDI data were regressed against the NILS DAFOR data, giving an r2 of 0.995
and the equation of the line of: DAFOR = 0.6165 x (log species weight) + 2.5265. This
equation was used to convert weights into the numerical DAFOR scale shown in Table 1.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 178
Table 1 Conversion of The ERTDI weights of macrophytes on the rake to a numerical
DAFOR scale.
Free and Little
Weights (g)
DAFOR Numerical
DAFOR scale
<0.43 Rare 1
0.43 - 2.16 Occasional 2
>2.16 - 10.90 Frequent 3
>10.90 - 55.26 Abundant 4
>55.27 Dominant 5
Combining physico-chemical data The NILS data had conductivity instead of alkalinity values, although the ERTDI had both.
Therefore the regression between alkalinity and conductivity in the ERTDI data set (r2 =
0.914) was used to convert the NILS conductivity into alkalinity.
Light absorption was measured as Hazen in the ERTDI data, but optical density at 340nm
(OD340) only was available in the NILS data. Optical density and Hazen were measured at
16 lakes with a range of transparency. This enabled the conversion of the NILS optical
density data to Hazen values (Figure 1). To convert from a 1cm cell at 455nm to a 5cm cell
equivalent at 430nm a conversion factor of 100/0.02655 had to be applied. Thus:
02655.01001932.0 340 ××= ODH
68.727340 ×= ODH
where:
OD340 = Optical density in a 1cm of sample at 340nm
OD455 = Optical density in a 1cm of sample at 455nm
H = Hazen value (mg L-1 Pt)
Figure 2 shows the location of the 619 sites used to create the CBASlak model.
Equation 1
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 179
y = 0.1932xR2 = 0.9825
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.00 0.10 0.20 0.30 0.40 0.50 0.60Absorbance at 340nm
Abso
rban
ce a
t 455
nm
Figure 1. Relationship between the optical density, 1cm at 455nm, used in calculating Hazen
values, and the optical density at 340 nm in a 1cm cell. Since zero values on both scales are
equivalent the intercept was forced through zero.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 180
Figure 2. Location of the 619 lake sites used to create the CBASlak model. 472 are from the
Northern Ireland Lake Survey (NILS) and 147 are in the Republic.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 181
3. Creating the CBASlak model The CCA model with the highest percentage explained variance of macrophyte species
relative abundance was a square root transform of the numerical DAFOR macrophyte
abundance, with down weighting of rare species (Table 2). (ter Braak 1987) suggests the
best model is the one that explains the highest percentage of species variance, without it
adversely affecting the axes eigen-values. Gradient lengths for this model are shown in
Table 3. One of the assumptions of CCA is that data is unimodal. Gradient length to
determine species distribution was checked prior to selecting CCA as a method (first three
axes had gradient lengths greater than 4). Table 4 shows the marginal effects (variance
explained by each variable individually) of all the variables available for creating the CCA
model. Table 5 shows the conditional effects (variance explained in addition to that explained
by preceding variables). EAST and NORTH were not considered suitable variables for
inclusion in the model as they have no causal ecological basis and COND and CHL
explained less additional variance than the variables shown in Table 5. All variables within
the model are significant at P = 0.05 (Bonferroni adjusted). Table 6 shows the weighted
correlation matrix from CANOCO for the variables in the model. Table 7 shows the CBASlak
ordination model summary. Figure 3 and 4 show the CBASlak CCA ordination plots. TP
species optima derived from the model were multiplied by 10 and rounded to zero decimal
places in order to produce an easily interpretable value (Appendix 1), as was done in
CBASriv. pH species optima were multiplied by -10 since a decrease in pH (acidification) is
considered to be an impact i.e. metric values should be scaled to increase with increasing
impact.
Table 2. Effect of macrophyte species transformations on explained species variance within
CBASlak
No down-weighting of rare species Down-weighting of rare species
Transform Canonical
eigen-
values
Total
inertia
% Variance
explained
Canonical
eigen-
values
Total
inertia
% Variance
explained
none
(DAFOR)
0.875 9.490 9.2 0.720 5.067 14.2
square-
root of
DAFOR
0.846 8.826 9.6 0.699 4.629 15.1
pres/abs
0.827 8.866 9.3 0.678 4.547 14.9
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 182
Table 3. Gradient lengths of CBASriv CCA model (square-root transformation of DAFOR,
down weighting of rare species).
Axis 1 Axis 2 Axis 3 Axis 4
Gradient length 4.447 7.289 7.267 3.554
Table 4. Marginal effects (individual variance explained) for variables available for the
creation of the CBASlak model. All variables are significant at P = 0.0001 (9,999
permutations). NB. Total nitrogen data was not available for The ERTDI data.
Abbreviation Variable Variance
explained (λ1)
ALK Alkalinity (meq/L) 0.284
EAST Easting 0.251
COND Conductivity (μs/cm) 0.244
TP Total phosphate (μg/l) 0.212
AREA Lake surface area (ha) 0.212
PH pH (pH units) 0.177
NORTH Northing 0.161
DEPTH Maximum depth (m) 0.113
CHL Chlorophyll a concentration (μg/l) 0.085
ALT Altitude of lake (m) 0.075
HAZEN Water colour (Hazen) 0.066
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 183
Table 5. Conditional effects (additional variance explained) for variables within the CBASlak
model. All variables within the model are significant at P = 0.0001.
Variable Variance explained (λA)
ALK 0.286
AREA 0.184
TP 0.095
PH 0.051
ALT 0.047
HAZEN 0.032
DEPTH 0.025
Table 6. Weighted Correlation Matrix from CANOCO. Correlation between the main metric
(TP) and alkalinity is highlighted.
Axis 1
Axis 2 0
Axis 3 0 0
Axis 4 0 0 0
ALK
-
0.817
-
0.416
-
0.361 0.042
PH
-
0.590
-
0.476 0.147
-
0.434 0.567
HAZEN
-
0.235 0.393
-
0.115 0.321
-
0.041
-
0.126
TP
-
0.714 0.011 0.517 0.183 0.387 0.290 0.372
ALT 0.056 0.542
-
0.093
-
0.683
-
0.281
-
0.166 0.170
-
0.054
AREA 0.554
-
0.766
-
0.034
-
0.079
-
0.150 0.006 -0.248
-
0.309
-
0.205
DEPTH 0.500
-
0.116
-
0.219
-
0.284
-
0.228
-
0.193 -0.419
-
0.444 0.025 0.374
Axis
1
Axis
2
Axis
3
Axis
4 ALK PH HAZEN TP ALT AREA DEPTH
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 184
Table 7. CBASlak summary table from CANOCO.
Axes 1 2 3 4 Total inertia
Eigenvalues 0.368 0.163 0.070 0.045 4.629
Species-environment
correlations 0.857 0.745 0.571 0.505
Cumulative percentage
variance
of species data 8.0 11.5 13.0 13.9
of species-environment relation 52.7 76.0 86.0 92.4
Sum of all eigenvalues 4.629
Sum of all canonical
eigenvalues 0.699
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 185
-1.0 1.0
-0.8
0.6
call brucall ham
call her
cera dem
char sp.
elat hex
elod canerio sep
fila alg
font
font ant
hipp vul
isoe lacjunc bul
lemn min
lemn tri
lito uni
lobe dor
moss aqumyri alt
myri spi
naja fle
nite sp.
nuph lut
nymp alb
othe alg
poly amp
pota alp
pota cri
pota gra
pota luc
pota nat
pota obt
pota pecpota per
pota pol
pota pus
pota sp
sagi sag
spar ang
spar eme
spar min
spha sp
utri int
utri min
zann pal
ALKPH
HAZEN
TP
ALT
AREA
DEPTH
0.0
0.1
0.2
0.3
0.4
1 2 3 4
-1.0 1.0
-1.0
0.8
ALK
PH
HAZEN
TP
ALT
AREA
DEPTH
Figure 3. CBASlak ordination model. Site conditional biplot. Axes 1 and 2. Histogram shows
eigen-values. The first two axes explain 76% of the (total canonical) variance.
Figure 4. CBASlak ordination model. Species conditional biplot. Axes 1 and 2. Only species
with a fit of 2% or greater are shown. The first two axes explain 76% of the (total canonical)
variance.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 186
4. The use of Abundance and Tolerance in CBASlak In the original CBAS method (Dodkins et al. 2005a) the metric value at a site was determined
by the mean optima of the species occurring at that site, weighted by the abundance and a
species tolerance value (derived from niche breadth). (ter Braak 1996) p.36 details the use of
tolerance in environmental calibration more fully.
In CBASriv, abundance and tolerance were found to produce no or very little improvement in
correlations between the metric value and the value representing an underlying impact
gradient. TP and pH metric scores were calculated at all the sites within the CBASlak data
set that had sufficient species data (608 out of 619). Weighting of: i. Abundance, ii.
Tolerance, iii. Abundance and tolerance, or iv. No weighting, was used to determine metric
scores. Correlation coefficients were then determined between the underlying environmental
gradients i.e. log TP and pH, and the metric scores (Figure 4).
Figure 4. Coefficient of determination between TP and PH metrics with their underlying
environmental gradient (log TP and pH respectively) using abundance and tolerance
weightings (internal validation).
Abundance
and tolerance Tolerance only
Abundance
only
No abundance
or tolerance
TP metric 0.492 0.488 0.486 0.481
PH metric 0.502 0.500 0.493 0.490
0.0
0.1
0.2
0.3
0.4
0.5
0.6
TP PH
r2 cor
rela
tion
abundance and tolerancetolerance onlyabundance onlyno abundance or tolerance
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 187
Figure 5. Correlation of TP and PH metrics with their underlying environmental gradient (log
TP and pH respectively) using abundance and tolerance weightings (external validation).
Abundance
and tolerance Tolerance only
Abundance
only
No abundance
or tolerance
TP metric 0.408 0.403 0.412 0.407
PH metric 0.456 0.448 0.449 0.438
Tolerance was more important than abundance as a weighting factor in the internal
validation, but the converse was true with external validation (Figure 3 and 4). Indeed,
removing the tolerance weighting improved the TP metric correlation with log TP in external
validation (Figure 5). Tolerance may reflect the frequency of species occurrence rather than
its environmental distribution, i.e. rare species may mistakenly be given a low tolerance as
they are only found at one or two sites. Thus, in internal validation the tolerance may improve
the model fit to the underlying environmental gradient, whereas in external validation the
tolerance has little relationship with the indicator ability of the species. Although abundance
was more important in the external validation, the benefit was very minor. The poor benefit of
abundance measures may be due to high natural temporal and spatial variation abundance
that is not adequately controlled in the sampling method. Also abundance may only be useful
at the very extreme of the impact (e.g. TP) gradient.
As neither abundance nor tolerance improved the metric scores they were excluded. This will
simplify the survey procedure and, based on external validation, would only cause a 0.008 %
0.00.10.10.20.20.30.30.40.40.50.5
TP PH
r2 val
ue
abundance and tolerancetolerance onlyabundance onlyno abundance or tolerance
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 188
decrease in the number of classes that can be significantly distinguished within the TP metric
and a 1.6 % decrease in the pH metric (using the equation in (Prairie 1996) and the
Ecological Quality and Status Bands section). With no weightings, the number of status
classes that can be significantly distinguished for both TP and PH metrics is between 1.7
(external validation) and 1.8 (internal validation).
5. Reference Conditions
Sixty-three reference lakes had been suggested for Ecoregion 17 from the Republic of
Ireland (Free et al., 2006), and 20 from Northern Ireland (McElarney, 2002). Seventeen
suggested reference lakes from RoI data have been confirmed to be at reference state using
paleolimnological assessment (Taylor et al., 2005) All these lakes were included in the 619
site CBASlak model, which was divided into the different lake types (Table 8) and the TP
metric was calculated for each lake.
Within each lake type, the lakes were sorted by total phosphorous concentration. Lakes with
the lowest TP metric value and at the lower end of the TP concentration were chosen as
reference sites. Lakes suggested as reference sites in the ERTDI study were given
preferential selection. An attempt was also made to ensure the reference lakes represented
the range of pH values and that the mean pH of the reference conditions was similar to the
mean pH throughout the lake type. It was considered that the reference network should
comprise about 10 % of the lake sites overall, although the distribution varied between lake
types. Table 9 shows the final list of reference lakes within each type along with mean TP
and pH values for the lake type.
Consideration was given to a reference site prediction method whereby optimal boundaries
of alkalinity, depth and surface area are determined based on which variables and
boundaries produce maximum internal similarity of TP within lake types (rather than species
similarity), using permutation (Dodkins et al. 2005b). From here a similar approach to the
RIVPACS method of species prediction (Wright 1995) could be adopted, assessing whether
a monitoring site belongs to one of the lake types and then determining the metric value by
weighting the mean reference metric value within the lake type by the probability of the site
having membership of that lake type. This approach is similar to krigging (see Exploring
changes to CBAS Section), in that it allows reference metric predictions to be influenced
more by sites with more similar environmental conditions, although this method enables
many variables to be used. However, only alkalinity and depth were necessary for defining
lake types in terms of the TP metric values at reference sites (and only alkalinity for the pH
metric), and thus the more accurate krigging could be done.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 189
Krigging was carried out using alkalinity and depth (Figure 6 and 7), but as with CBASriv it
was found that there was a good linear relationship between log alkalinity or log depth with
the TP and pH metric value at reference sites. Thus Multiple Linear Regression (MLR) was a
simpler model than krigging and would also enable ‘in the field’ reference condition
prediction. Table 10 shows the coefficients and statistics derived from the MLR. Prediction of
the reference condition pH metric value does not significantly increase with the inclusion of
depth as a predictor (r2 goes from 0.771 to 0.779), and therefore only alkalinity is necessary
to predict the pH metric. Lake size did not significantly improve reference condition
predictions for either the TP or pH metric.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 190
Table 8. Ecoregion 17 Lake Typology. The value in the table is the lake type. (Environmental
Protection Agency 2005b). (*) 50 mg/l CaCO3 is equivalent to 1 meq/l (Wetzel 2005).
Alkalinity (mg/l CaCO3)*
Mean depth (m) Size (ha) Low (<20) Mod (20-100) High (>100)
Shallow (≤4) Small (≤50) 1 5 9
Large (>50) 2 6 10
Deep (>4) Small (≤50) 3 7 11
Large (>50) 4 8 12
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 191
Table 9. Reference sites used in CBASlak. (*) Indicates paleolimnologically confirmed
reference site. Lakes in Class 5 and 6 may be impacted, and further review is required to
replace them with more suitable alternatives.
alk class
depth class
size class
Lake Class Lake name
Mean TP
(ug/l)Mean
pH Easting Northing
CBAS lake no.
Lough A Waddy 204100 364400 961 1 Lough Wee 14.0 7.0 198900 364600 89
1 Loughanillan 257500 379500 49Cloneemiddle 81000 64000 504
2 2 Glanmore 14.0 6.9 77500 55000 543Nagravin 99000 221500 584Barfinnihy 85000 76800 489 *Cloongat 68900 247200 505Doo(DL) 235900 439400 522 *Glencullin 81900 269600 547
1 3 Hibbert 7.0 6.6 88200 222300 5541 Nahasleam 97100 244000 585 *
Naminn 239600 441900 588Naminna 117600 171000 589
2 Waskel 173800 416100 618Currane 53000 66000 513Dan02 315000 204000 515 *Doo(MO) 83300 268200 523 *Dunglow 178200 411700 529 *
2 4 Feeagh 5.0 4.9 96500 300000 537Guitane01 102500 84500 552Shindilla 96000 246000 605 *Upper 90000 81700 615 *Veagh 201700 421100 617 *Aughnagurgan Lough 287400 331100 331Clabby Lough 241400 349400 158Fir Lough 201300 364900 93Glencreawan Lough 202500 356500 86
1 5 Killelagh Lough 20.0 7.4 283400 402600 151 Loughnabrick 325800 419900 23
Loughnatrosk 327300 419900 24Tamnymore Reservoir 243200 414600 9Toppedmountain Lough 230900 345200 152
2 6 Alewnaghta 25.0 8.1 175900 191100 4742 Bofin(Shannon) 204000 288500 493
Ballynakill(Gorumna) 86500 222200 487Carrigeencor 183000 333600 498
1 7 Killylane Reservoir 12.0 7.7 329000 398400 27Kiltooris 167500 397000 562 *Lough Narye 239800 333800 218
2 Rowan 208500 306000 600Glencar 175000 343500 546Keel(Achill) 65000 306000 558 *
2 8 Kindrum 14.0 8.0 218500 443000 564 *McNean 203200 339700 573 *Melvin 190000 354000 575Talt 139800 315000 611Atedaun 129500 188500 480Drumaveale Lough 247300 319600 279
1 9 Glore 25.0 8.1 248900 271900 5481 Islandhill Lough 254200 330700 248
Rathkeevan Lough 253900 330300 249Un-named (Glastry) 363900 363000 404
2 10 Annaghmore 8.0 8.5 190000 283700 4773 O'Flynn 158500 279500 591 *
Ballyeighter 134000 139000 485Clonlea 151000 173500 499
1 11 Cullaun02 12.0 8.3 131500 190500 511Lough Garrow 243500 319000 277
2 Summerhill Lough 249000 328000 256Bane02 255000 271200 488Bunny02 137500 196700 496 *
2 12 Lene02 6.0 8.5 251000 268500 568 *Muckanagh02 137000 192500 577 *Rea 161500 215500 596 *
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 192
Figure 6. Krigged TP metric value with alkalinity as the x-axis and depth (x 7) as the y-axis.
Figure 7. Krigged pH metric value with alkalinity as the x-axis and depth (x 7) as the y-axis.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 193
Table 10. Multiple Linear Regression Coefficients and statistics for prediction of TP and pH
metric values at reference condition. Within the predictive equations depth is mean depth in
meters, and alkalinity in meq/l.
Metric
r2 in MLR
Predictive equation for reference condition
Mean
Standard Error
of Prediction
TP 0.677 (3 x log alkalinity) - (3.3 x log depth) -2.6 ± 0.46
PH 0.771 (-5.5 x log alkalinity) + 2.6 ± 0.39
6. Calculation of EQR The Ecological Quality Ratio (EQR) is calculated in the same way as in mCBASriv. First the
species that occur at the monitoring site are recorded and the mean optima of these species
is selected from Appendix 1. Then, using the reference condition predictive equations (Table
10) the reference metric value for TP is calculated. The reference metric value is subtracted
from the mean species optima to produce the impact metric score. The same process is
undertaken for the pH metric. Since the TP and pH metrics only have a correlation of 0.290
they could potentially be added together to get a field estimate of total ecological change
(TEC). This is scaled in 10ths of a standard deviation in species turnover (i.e. 40 units infers
there is no species in common between the monitoring and reference site). To convert the
field method TEC to an EQR equation 2 is used. This equation was derived by finding the
maximum expected TEC within the 619 site data set. Although there was a lake with a TEC =
35.1, this and another high scoring lake were considered to be outliers and therefore the next
worst lake (TEC = 24.9) was used.
2525 f
f
TECEQR
−=
where:
EQRf = Ecological quality ratio derived from field method
TECf = Total Ecological Change derived from field method
The decomposition method of TEC calculation is more accurate as it removes the correlation
between the TP and pH metrics (Dodkins et al. 2005a): i. the number of orthogonal axes in
the CBASlak model that explain most of the species variance is determined; this is judged to
be only the first two axes (Table 7). ii. The TP and pH impact metric scores for a site are
each multiplied by their correlation coefficient with the first and second ordination axes.
Equation 2
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 194
These correlations are available from the CBASlak model (Table 6). Negative correlation
coefficients are made positive since the metric is already scaled to increase with impact. iii.
After adjustment by the correlation with the axis, the maximum value along the 1st and 2nd
axis is determined. iv. Since the axes are orthogonal, these maximum values are added
together to produce TEC.
Ignoring the two outliers, the maximum TEC using the computer method was 15.8. Therefore
to convert TEC from the decomposition method to EQR equation 3 is used.
1616 d
dTEC
EQR−
=
where:
EQRd = Ecological quality ratio derived from the decomposition method
TECd = Total Ecological Change derived from the decomposition method.
EQR values determined by the field method and by the computer method for the 619 sites in
the CBASlak model were plotted against each other (Figure 8). This shows that, although the
field method of TEC calculation overestimates the impact, when converted to EQR there is
little difference in the results (r2 = 0.998, slope = 1.0, intercept = 0.0). The frequency
distribution of EQR when divided into five even classes is shown in Figure 9. The minimum
EQRf for the reference sites is 0.75.
The results suggest that the field method of EQR calculation is adequate. If the ecological
status categories are evenly divided along the EQR range (Figure 9) most of the reference
sites fit into high status, although a few are good status (though, as detailed in the ‘Ecological
Quality Status Bands and Errors’ report, this can be justified since there need not be a
significant difference between high and good status). Most sites fit into the good status
category, and there are very few at poor or bad status. There should be further consideration
as whether to change the position of status boundaries to ensure more sites occupy poor
and bad status categories, based on wider consultation and possibly field experience when
applying the mCBASlak method. Figure 9 shows the distribution of sites in different status
boundaries from the whole CBASlak data set.
Equation 3
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 195
Figure 8. EQR calculated from the field method (EQRf) plotted against that calculated using
decomposition (computer method) (EQRd), using the 619 sites from the mCBASlak model.
y = 0.9667x + 0.0369R2 = 0.9982
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0EQRd
EQR
f
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 196
0
50
100
150
200
250
300
bad
poor
mod
erat
e
good high
EQRf categories
Freq
uenc
y
Figure 9. Frequency distribution of EQR calculated using the field method for the 619 sites in
the mCBASlak model, and an even distribution of EQR into classes (Table 12).
7. Ecological Quality Status Bands and Errors Previous work with CBASriv suggested that the ability to determine error through comparing
the underlying gradient (e.g. log TP) with the metric (TP metric) was not viable since the
biologically derived metric value is probably a more robust and reliable indicator of general
impact than the physico-chemistry data. The necessity for further work on the complex issue
of error estimation was also underlined.
In order to give an initial error estimate, 95% and 90% confidence intervals were determined
for the pH and TP metrics when plotted against the underlying impact gradient (Figure 10
and 11). This is an over-estimate of error, since
i. biology is a better indicator of general impact than chemistry and
ii. lake types have not been taken into account.
Although theoretically accounting for the river or lake type should improve the ability to detect
impact, since there is not a valid gradient to compare this with (chemistry is too unreliable,
especially when ‘reference chemical conditions’ are estimated), this assessment was not
undertaken for CBASlak (see ‘Validation of CBASriv’ section). The significance of the
regression line and Root Mean Square Error (RMSE) were calculated. These statistics were
determined using both internal and external validation (the same split data set as before)
(Table 11).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 197
Table 11. Correlation coefficients and confidence intervals for TP and pH metrics when
plotted against log TP and pH with internal and external validation. Due to the large number
of data points and the method of metric development all regressions are highly significant (P
< 5 x 10-59).
Internal validation External validation
TP metric pH metric TP metric pH metric
Maximum metric value6 19 6 16
Minimum metric value -15 -6 -16 -8
r2 value 0.466 0.484 0.403 0.448
RMSE 3.16 2.83 3.61 3.20
Confidence intervals
95% 6.21 5.56 7.08 6.28
90% 5.21 4.66 5.94 5.27
sig. of line p = <0.001 <0.001 <0.001 <0.001
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 198
Figure 10. Regression of the TP metric value at the 619 CBASlak sites against log TP
concentration, showing 90% (inner) and 95% (outer) confidence intervals for an individual
site i.e. ± 5.2 and ± 6.2 respectively.
Figure 11. Regression of the pH metric value at the 619 CBASlak sites against pH, showing
90% (inner) and 95% (outer) confidence intervals for an individual site i.e. ± 4.7 and ± 5.6
respectively.
R2 = 0.481
-20
-15
-10
-5
0
5
10
15
20
0 0.5 1 1.5 2 2.5 3 3.5 4
log TP (mg/l)
TP m
etri
c (S
D o
f spp
. tur
nove
r x
10)
R2 = 0.490
-15
-10
-5
0
5
10
15
20
25
4 5 6 7 8 9 10
pH
PH m
etri
c (S
D o
f spp
. tur
nove
r x
10)
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 199
The inability of spot chemical sampling to represent the general impact gradient is likely to
result in an over-estimate of the error and thus (externally validated) RMSE may be a better
approximation of error than 95 % confidence intervals (since it is lower) until further methods
of error estimate have been developed. Thus the TP and pH metrics have an error of ± 3.6
and 3.2 respectively. Since it is difficult to estimate error for total ecological change (TEC)
(see ‘Validation of CBASriv report) error terms can be multiplied by the correlation between
the TP and pH metrics (using the same method as decomposition) to get an error value of ±
4.1. For the field method of TEC calculation the error values should be added i.e. ± 5.8. This
can be converted to error values for the EQR calculation by dividing by 16 and 25
respectively (equations 2 and 3), i.e. error for EQRd is ± 0.26 and for EQRf is ± 0.23. The
combination of EQR error values in this way is not appropriate, especially since it suggests
the field method of EQR has less associated error (which may not be true). However, there is
no method to accurately determine an underlying ‘EQR impact gradient’ using the available
data, against which the CBASlak EQR can be tested, so these values can be considered
reasonable estimates.
Until intercalibration is finished, the even split of EQR into status categories is suggested to
produce an even division of error (i.e. minimise miss-classification) (Table 12). Although the
lowest EQR value for a reference site is 0.75 this still falls within the high/good class which
are considered by the WFD to not be significantly different (see ‘Ecological Quality Status
Bands and Errors’ report). This is also well within the high status ± the EQR error (0.23).
Currently an EQR of 0.6 - 0.23 = 0.37 or less could be legally justified as being a significant
deviation from good status.
Table 12. Suggested status boundaries, spreading error evenly throughout the EQR scale.
Status EQR range
High > 0.8 - 1.0
Good >0.6 - 0.8
Moderate > 0.4 - 0.6
Poor > 0.2 - 0.4
Bad ≤0.2
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 200
8. Comparison with equivalent methods Lake Macrophyte Nutrient Index (LEAFPACS for lakes) A thorough comparison of CBASriv and LEAFPACS for rivers was previously undertaken,
supporting the theoretical assumption that CBAS would perform better (Validation of CBASriv
section). Within Great Britain (Ecoregion 18) the LEAFPACS approach has also been applied
to macrophytes within lakes. The Trophic Ranking Score (TRS) (Palmer and Roy 2001) was
recalibrated by Nigel Willby, improving the r2 value in a regression of TRS against log
summer TP, from 0.19 to 0.33. The resultant correlation of this ‘Lake Macrophyte Nutrient
Index’ (LMNI)) with TP is lower than the correlation of the CBASlak TP metric with TP when
externally validated (0.403). In addition TRS was considered to be correlated with alkalinity
rather than TP. Due to the time taken to develop the LMNI method for Ireland, and following
recent discussions with Nigel Willby there were proposals to collaborate in order to compare
these methods more fully in the future. A fuller comparison within this report will be
premature but comparisons will be published when the LEAFPACS and LMNI methods are
finalised.
Gary Free’s Metrics The Plant Trophic Score developed by Gary Free was highly correlated with log TP within his
own data i.e. internal validation (Pearsons correlation coefficient = 0.68). In comparison the
CBAS TP metric has a Pearsons correlation with log TP of 0.69, which is only slightly better.
This is suprising since Free’s method is based on Weighted Averaging whereas CBAS is
based on Reciprocal Averaging. It has recently been considered by the author that the large
improvement of LEAFPACS over MTR or Weighted Averaging methods in rivers is due to
Reciprocal Averaging (which is the basis of the recalibration method in LEAFPACS).
However Free’s correlation is based only on lakes with an alkalinity of >20 mg/l and with only
93 sites (compared to 619 in CBAS). With such low numbers of sites external validation is
likely to highly over-estimate the real success of the Free score, thus Free’s Plant Trophic
Score (PTS) and the CBAS TP metric were regressed against log TP for all 609 sites in the
CBAS database (4 lakes could not be calculated with Plant Trophic Score due to lack of
indicator species).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 201
Figure 12. Log TP regressed against the CBAS TP metric and Free’s Plant Trophic Score.
Figure 13. Log alkalinity regressed against the CBAS TP metric and Free’s Plant Trophic
Score.
Since metrics may be indicating alkalinity, rather than anthropogenic impact, the CBAS TP
metric and the PTS were also regressed against log alkalinity (Figure 13).
R2 = 0.489
-20-15-10-505
1015
0 1 2 3 4
log TP (μg/l)
CB
AS
TP m
etri
c
R2 = 0.452
020406080
100
0 1 2 3 4
log TP (μg/l)
Free
's P
lant
Tro
phic
Sc
ore
R2 = 0.429
-20-15-10-505
10
-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0
log alkalinity (meq/l)
CB
AS
TP m
etri
c
R2 = 0.211
020406080
100
-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0
log alkalinity (meq/l)
Free
's P
lant
Tro
phic
Sc
ore
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 202
Figure 12 shows that the CBAS TP metric is more highly correlated with log TP than Free’s
PTS. The coefficient of determination with CBAS is 0.489 (can distinguish 1.83 classes (see
Ecological Quality Status Bands and Errors section)), whereas with the PTS it is 0.452 (can
distinguish 1.77 classes). However, suprisingly the PTS has very little correlation with log
alkalinity (coefficient of determination =0.211, compared to 0.429 for CBAS). Figure 14
shows a CCA ordination (as Figure 3) with the PTS and CBAS TP metric overlayed as
supplementary (passive) environmental variables i.e. they do not affect the model. A strong
correlation is observed between log TP and the PTS and CBAS TP metrics, with the CBAS
TP metric expressing more species variance. Figure 15 shows the same ordination but with
axes 2 and 3. This illustrates that the CBAS metric is less correlated with TP along the third
axis.
Therefore, CBAS has a stronger correlation with log TP than the PTS, but much of this
response could be due to alkalinity rather than TP, whereas this is less likely with PTS.
CBAS does not require abundance to be recorded (whereas PTS does) potentially saving
considerable time. The CBAS method does fulfil WFD requirements to utilise abundance
through a basic additional metric, although the abundance measure is much easier to
estimate than with the PTS method. It is likely that the omission of abundance is the reason
for the separation between the TP gradient in the model and the CBAS TP metric. PTS is
also suprisingly effective considering it only utilises 42 species, compared with 95 within
CBAS. In only 4 lakes out of the 609 lake data set species weren’t available to be able to
calculate PTS.
Conclusion
Although CBAS has a higher correlation with log TP and does not require accurate
abundance measures, PTS is a very competitive method with the advantage of a low
correlation with alkalinity and a much smaller species list. This is very suprising since
reciprocal averaging (used in the CBAS method) should have a large advantage over
weighted averaging. It is therefore possible that PTS could be used as a basis for a metric,
but improved further using reciprocal averaging and inclusion of additional species (in the
same way that LEAFPACS is used to recalibrate MTR).
CBAS does enable additional metrics (such as zone of extinction) to be appended, although
additional metrics within PTS appear effective at producing good combined correlation with
log TP In Free’s method additional metrics combine into a single index which has a
Pearson’s correlation of 0.77 with log TP (n = 92).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 203
.
Figure 14. Axes 1 and 2 of CCA of sites used to create the CBAS model (as Figure 3) with
the CBAS TP metric (CBAS TP) and Free’s Plant Trophic Score (PTS) overlayed as
supplementary variables.
Figure 15. Axes 1 and 3 of CCA of sites used to create the CBAS model (as Figure 3) with
the CBAS TP metric (CBAS TP) and Free’s Plant Trophic Score (PTS) overlayed as
supplementary variables.
-1.5 1.0
-0.8
0.6
ALKPH
HAZEN
TP
ALT
AREA
DEPTH
PTSCBAS TP
-1.5 1.0
-0.4
0.6
ALK
PH
HAZEN
TP
ALT
AREA
DEPTH
PTS
CBAS TP
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 204
9. Conclusions
Lake surveys are time-consuming and labour intensive and therefore costly. CBAS can
reduce these costs through reducing the necessity of measure abundance accurately, whilst
still producing an effective metric. However Free’s Plant Trophic Score (PTS) has
comparable success in reflecting a log TP gradient, but with less correlation with alkalinity.
Theoretically the CBAS approach should be more effective at separating the gradients, so
the success of the PTS may be due to the division of the sites into separate bands (allowing
non-linear responses across the TP gradient as a whole).
LEAFPACS has undergone continuing development in light of discussions between Dodkins
and Willby, and new macrophyte methods are still being advanced within Europe
(Szoszkiewicz et al. accepted for publication 2006). There are plans for further comparisons
between methods, which will be published in the scientific literature. It is highly likely that
none of the methods described here produce an optimum lake metric, and that further
discussion and research could help to determine the benefits of each system to produce a
hybrid which performs better than any one system alone. Currently Free’s macrophyte index
appears to be most successful for lakes, but it is expected that reciprocal averaging within
the TP bands would improve the performance of the PTS metric further. There may also be
future metric developments which could contribute to a multi-metric system, particularly to
indicate acidification.
It would be useful to determine how to reduce surveying effort in lakes to enable the same
information to be gathered at lower cost. This will be dependent on the final method used.
Macrophyte abundance relative to the area of the photic zone may also help to separate
abundance responses due to trophic status from that due to available habitat area.
Accurate determination of the error for the EQR is difficult and requires further study.
Feedback from field study, and data collected throughout the monitoring network of
Ecoregion 17 as well as more collaboration between developers of different methods (now
preliminary methods have been developed) will lead to further improvements.
It is recommended that in the first instance Free’s macrophyte Index is adopted for lakes, but
that the measurement of ecological status is seen as an on-going process, allowing continual
method improvement and the creation of additional metrics.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 205
Appendices
1. Minimum species list and optima
2. Field survey sheets for in the field calculation
3. Recommendations for future improvement to the method
For examples of calculating metric scores and EQR see the CBASriv Scientific Summary
(Appendix 4).
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 206
Appendix 1. Minimum macrophyte species list and CBASlak species optima. Mean
optima at a site are used to determine mean metric score. Scores increase with impact. Units
are 1/10 th SD of species turnover.
TP PH TP (cont.) PH (cont.)
-23 spar min -16 pota x l 0 ranu pel 0 cras hel
-22 ranu pen -15 ranu bau 0 nymp alb 0 elod spp
-20 utri sp. -15 elod cal 0 call sp. 0 myri aqu
-19 isoe ech -11 call pla 1 live aqu 0 spar eme
-19 erio sep -11 zann pal 1 spar eme 0 pota obt
-16 elat hex -10 call obt 1 nymp pel 0 pota gra
-16 call ham -9 laga maj 1 pota ber 0 pota pra
-15 pota x n -9 cera sub 1 pota fil 1 font
-14 isoe lac -9 pota pec 2 call her 1 apiu inu
-14 naja fle -8 cera dem 2 hydr mor 1 pota sal
-13 utri int -7 myri spi 2 elod can 1 elat hyd
-13 lobe dor -7 pota fil 2 elod cal 2 spar nat
-13 junc bul -7 poly amp 2 nuph lut 2 stra alo
-12 pota sp -7 ranu spp 3 stra alo 2 call bru
-11 font ant -6 ranu aqu 3 ranu aqu 2 nymp alb
-11 fila alg -6 ranu cir 3 othe alg 2 fila alg
-10 pota sal -6 lemn pol 3 lemn tri 2 utri vul
-10 groe den -5 eleo aci 3 call sta 3 pota x z
-10 utri min -5 pota cri 3 call bru 3 pota alp
-9 moss aqu -5 pota pus 4 ranu spp 3 myri alt
-9 myri ver -5 sagi sag 4 pota obt 3 pota nat
-8 eleo flu -4 hipp vul 4 elod nut 4 call her
-8 pota pol -3 pota per 5 poly amp 4 pota x n
-8 utri vul -3 pota col 5 eleo aci 4 myri ver
-8 spha sp -3 ranu pel 5 pota pec 4 lito uni
-7 spar ang -3 lemn tri 6 pota x l 5 eleo flu
-7 cras hel -3 elod can 6 cera dem 7 spar ang
-7 elod spp -3 pota sp 6 pota cri 8 font ant
-7 myri aqu -2 pota luc 6 myri spi 10 pota pol
-7 nite sp. -2 othe alg 6 call obt 10 moss aqu
-7 lito uni -2 nite sp. 6 lemn min 10 utri min
-6 pota col -2 call sp. 7 pota pus 11 naja fle
-6 myri alt -1 pota ber 9 zann pal 13 utri sp.
-6 spar nat -1 hydr mor 9 elat hyd 13 groe den
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 207
Appendix 1. Minimum macrophyte species list and CBASlak species optima (Cont) -5 pota luc -1 lemn min 10 call pla 14 junc bul
-4 pota gra -1 nymp pel 11 ranu cir 14 elat hex
-4 pota x z -1 char sp. 13 lemn pol 15 lobe dor
-4 laga maj -1 pota fri 17 isoe lac
-4 char sp. -1 call sta 18 live aqu
-3 sagi sag -1 nuph lut 19 ranu pen
-3 pota fri -1 elod nut 19 erio sep
-3 font 19 spha sp
-3 ranu bau 19 utri int
-2 pota nat 20 call ham
-2 hipp vul 21 spar min
-2 pota per 23 isoe ech
-2 apiu inu
-1 pota alp
-1 pota pra
-1 cera sub
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 208
Appendix 2. Preliminary Lake survey field sheet (print double sided)
TP PH Additional Species□ Apium inundatum -2 1 □ Alisma lanceolata□ Callitriche brutia 3 2 □ Alisma plantago-aquatica□ Callitriche hamulata -16 20 □ Butomus□ Callitriche hermaphrodita 2 4 □ Carex rostrata□ Callitriche obtusangula 6 -10 □ Carex versicaria□ Callitriche platycarpa 10 -11 □ Equisetum fluviatile□ Callitriche stagnalis 3 -1 □ Lemna gibba□ Ceratophyllum demersum 6 -8 □ Mentha aquatica□ Ceratophyllum submersum -1 -9 □ Menyanthes trifoliata□ Chara sp. -4 -1 □ Phragmites□ Crassula helmsii -7 0 □ Schoenoplectus□ Elatine hexandra -16 14 □ Typha latifolia□ Elatine hydropiper 9 1 □□ Eleocharis 5 -5 □ Cladophora□ Eleogiton fluitans -8 5 □ Fontinalis squamosa□ Eriocaulon septangulare -19 19 □□ Groenlandia densa -10 13□ Hippuris vulgaris -2 -4□ Hydrocharis morsus-ranae 2 -1□ Isoetes echinospora -19 23□ Isoetes lacustris -14 17□ Juncus bulbosus -13 14□ Lagarosiphon major -4 -9□ Lemna minor 6 -1□ Lemna polyrrhiza 13 -6□ Lemna trisulca 3 -3□ Litorella uniflora -7 4□ Lobelia dortmanna -13 15□ Myriophyllum alterniflorum -6 3□ Myriophyllum aquaticum -7 0□ Myriophyllum spicatum 6 -7□ Myriophyllum verticillatum -9 4□ Najas flexilis -14 11□ Nitella sp. -7 -2□ Nuphar lutea 2 -1□ Nymphea alba 0 2□ Nymphoides peltata 1 -1□ Polygonum amphibium 5 -7□ Pot. alpinus -1 3□ Pot. berchtoldii 1 -1□ Pot. coloratus -6 -3□ Pot. crispus 6 -5□ Pot. filiformis 1 -7□ Pot. friesii -3 -1□ Pot. gramineus -4 0
adverse weather conditions? Y / N
Location type % Substrate Neighbouring landuse (w ithin 15m)inlet clay gravel/pebble broadleaved woodland rough grasslandoutlet peat cobble coniferous woodland improved grasslandembayment earth boulder scrub and shrubs grazed grasslandexposed area silt bedrock wetland tilled landisland sand moorland rock/screeother (list) urban/suburban
LAKE SURVEY CBAS2006
day month yr
Lake No: Date: 06 Surveyors:
Lake name: upstream grid ref (GPS):
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 209
TP PH Notes□ Pot. lucens -5 -2□ Pot. natans -2 3□ Pot. obtusifolius 4 0□ Pot. pectinatus 5 -9□ Pot. perfoliatus -2 -3□ Pot. polygonifolius -8 10□ Pot. praelongus -1 0□ Pot. pusillus 7 -5□ Pot. salicifolius -10 1□ Pot. x lintonii 6 -16□ Pot. x nitens -15 4□ Pot. x zizii -4 3□ Ranunculus aquatilis 3 -6□ Ranunculus baudotti -3 -15□ Ranunculus circinatus 11 -6□ Ranunculus peltatus 0 -3□ Ranunculus penicillatus -22 19□ Sagittaria sagittifolia -3 -5□ Sparganium angustif. -7 7□ Sparganium emersum 1 0□ Sparganium natans -6 2□ Stratiotes aloides 3 2□ Utricularia sp. -20 13□ Zannichellia palustris 9 -11□ Elodea canadensis 2 -3□ Elodea nuttallii 4 -1□ Fontinalis antipyretica -11 8□ Sphagnum spp -8 19□ liverworts 1 -18□ other aquatic mosses -9 -10□ Filamentous algae -11 2□ Other algae 3 -2
TP PH1. Mean Optima Value 2 d.p.
equations for reference a log10 alk (meq/l)condition prediction
d log10 max depth (m)Expected at ref. condition
2. IMPACT metric (= Observed - Expected)
nutrient acidity3. General impact metric
sum (ignoring negative values)4. TEC (Total Ecological Change)
EQR = EQR Status> 0.8 High>0.6 - 0.8 Good
5. EQR 2 d.p. > 0.4 - 0.6 Moderate> 0.2 - 0.4 Poor
6. STATUS ≤ 0.2 Bad
25
3a - 3.3 d -2.6
-5.5a +2.6
25 - TEC
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 210
Appendix 3 - Recommendations for further improvements
Macrophyte field surveys should include as a minimum, measurements of:
1. Depth
2. Alkalinity
3. Lake area
4. Altitude
5. pH
6. Total phosphorous
7. Total nitrogen
8. Hazen value
9. Temperature
10. Slope of survey section
Field survey methods need to be further investigated to ensure maximum information is
being obtained with minimum effort.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 211
References Adler, R. W. 1995. Filling the gaps in water quality standards: legal perspectives on
biocriteria. Pages 345-358 in W. S. Davis and T. P. Simon, editors. Biological
assessment and criteria. Tools for water resource planning and decision making.
Lewis Publishers, Boca Raton, Florida.
Anderson, M. J., and T. J. Willis. 2003. Canonical Analysis of Principal Coordinates: a useful
method of constrained ordination for ecology. Ecology 84:511-525.
Angermeier, P. L., and J. R. Karr. 1994. Biological integrity versus biological diversity as
policy directives. Bioscience 44:690-697.
Armitage, P. D. 2000. The potential of RIVPACS for predicting the effects of environmental
change. Pages 93-111 in J. F. Wright, D. W. Sutcliffe, and M. T. Furse, editors.
Assessing the biological quality of fresh waters. Freshwater Biological Association,
Ambleside, Cumbria.
Arts, G. H. P. 2002. Deterioration of atlantic soft water macrophyte communities by
acidification, eutrophication and alkalinisation. Aquatic Botany 73:373-393.
Barbour, M. T., J. B. Stribling, and J. R. Karr. 1995. Multimetric approach for establishing
biocriteria and measuring biological condition. in W. S. Davis and T. P. Simon,
editors. Biological assessment and criteria. Tools for water resource planning and
decision making, Boca Raton, Florida.
Barbour, M. T., and C. O. Yoder. 2000. The multimetrics approach to bioassessment, as
used in the United States of America. in J. F. Wright, D. W. Sutcliffe, and M. T. Furse,
editors. Assessing the Biological Quality of Fresh Waters: RIVPACS and other
techniques. Freshwater Biological Association, Ambleside.
Bartram, J., and R. Ballance. 1996. Water Quality Monitoring. Chapman and Hall, London.
CEN. 2003a. Draft guidance standard for the surveying of macrophytes in lakes. CEN/TC
230/WG 2/TG 3/N72, Comité Europeén de Normalisation.
CEN. 2003b. Guidance standard for the surveying of aquatic macrophytes in running waters.
EN 14184, Comité Europeén de Normalisation.
CEN. 2004. Water Quality - Guidance standard on the design of multimetric indices. (Draft),
Comité Europeén de Normalisation.
Centre for Ecology and Hydrology. 2005. STARBUGS - STAR Bioassessment Uncertainty
Guidance Software.http://www.ceh-
nerc.ac.uk/products/software/software_starbugs.html.
Ciecierska, H. 1997. Synanthopization index as a measure of structural and spatial changes
in the process of aquatic vegetation synanthropization. Pages 233-361 in T. Puszkar,
editor. Current directions of ecology, behavioural ecology (in Polish), Lublin, Poland.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 212
Ciecierska, H. 2004. Ecological state of reference lakes of the European Intercalibration
Network, located in the Masurian Landscape Park (NE Poland). Limnological Review
4:45-50.
Clarke, R. 2000. Uncertainty in estimates of biological quality based on RIVPACS. Pages 40-
54 in J. F. Wright, D. W. Sutcliffe, and M. T. Furse, editors. Assessing the biological
quality of fresh waters: RIVPACS and other techniques. Freshwater Biological
Association, Ambleside, Cumbria.
Commission of the European Communities. 1995. Wise use and conservation of wetlands.
Communication from the Commission to the Council and the European Parliament.
COM (95) 189 final, 29.05.95, Commission of the European Communities, Brussels.
Council of the European Communities. 2000. Directive of the European Parliament and of
the Council establishing a framework for Community action in the field of water policy.
L327. Official Journal of the European Communities 43:1-73.
Council of the European Communities. 2005. Overall Approach to the Classification of
Ecological Status and Ecological Potential. 13 (guidance document) for the Common
implementation strategy for the Water Framework Directive (2000/60/EC), Official
Publications of the European Communities, Luxemboourg.
Dawson, F. H., J. R. Newman, M. J. Gravelle, K. J. Rouen, and P. Henville. 1999.
Assessment of the trophic status of rivers using macrophytes. Evaluation of the Mean
Trophic Rank. E39, Environment Agency, Bristol.
Demars, B. O. L., and D. M. Harper. 1988. The aquatic macrophytes of an English lowland
river system: assessing the response to nutrient enrichment. Hydrobiologia 384:75-
88.
Descy, J. P. 1979. A new approach to water quality estimation using diatoms. Nova Hedwigia
64:305-323.
Deshon, J. E. 1995. Development and application of the Invertebrate Community Index (ICI).
in W. S. Davis and T. P. Simon, editors. Biological assessment and criteria. Tools for
water resource planning and decision making, Boca Raton, Florida.
Dodkins, I, and B.Rippey (submitted 2007) CBAS – A method of measuring ecological status.
Water Research.
Dodkins, I., B. Rippey, and P. Hale. 2005a. An application of canonical correspondence
analysis for developing ecological quality assessment metrics for river macrophytes.
Freshwater Biology 50:891-904.
Dodkins, I. R. 2003. Developing a macrophyte index of ecological status for Northern
Ireland's rivers. PhD Thesis, University of Ulster, Coleraine.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 213
Dodkins, I. R., and B. Rippey. 2005a. A Review of Methods for Assessing Ecological Status
of Rivers and Lakes Using Aquatic Macrophytes Within the Water Framework
Directive. unofficial internal report, NS SHARE, University of Ulster, Coleraine.
Dodkins, I. R., and B. Rippey. 2005b. Survey Methodology for River Macrophytes (River
Field Methods and Minimum Species List). unofficial internal report, NS SHARE,
University of Ulster, Coleraine.
Dodkins, I. R., B. Rippey, and P. Hale. 2003. The advantage of metrics for aquatic
macrophyte assessment in Northern Ireland. Temanord 547:29-34.
Dodkins, I. R., B. R. H. T. Rippey, T. J. Harrington, C. Bradley, B. Ni Chathain, M. Kelly-
Quinn, M. McGarrigle, S. Hodge, and D. Trigg. 2005b. Developing an optimal river
typology for biological elements within the Water Framework Directive. Water
Research 39:3479-3486.
Edwards, A. L. 1976. An introduction to linear regression and correlation. W.H. Freeman and
Co., San Francisco.
Environment Agency. 2002. Report Assessment and Management (RAM) Framework -
Report and User Manual. Version 3. R&D technical manual W6-066M, Environment
Agency, Almondsbury, Bristol.
Environmental Protection Agency. 2002. Standard operating procedure - procedure for
macrophyte sampling (surveillance monitoring of lakes). Pilot Study 2002-FS-1-M1,
Johnstown Castle Estate, County Wexford.
Environmental Protection Agency. 2004. Reference Conditions for Irish Rivers - Description
of River Types and Communities. draft report. http://www.wfdireland.ie/, EPA, Co.
Wexford, Ireland.
Environmental Protection Agency. 2005a. The Characterisation and Analysis of Ireland’s
River Basin Districts (for Artical 5 of the WFD), Johnstown Castle Estate, County
Wexford.
Environmental Protection Agency. 2005b. Summary note of Irish lake typology to be applied
in Ireland's river basin districts - surface water guidance document. EPA, Dublin.
http://www.wfdireland.ie/.
Environmental Protection Agency. 2005c. Water Framework Directive - Characterisation of
Reference Conditions and Testing of Typology of Rivers. ERTDI Report No. 31,
Environmental Protection Agency, Dublin, Ireland. Also at:
http://www.epa.ie.EnvironmentalResearch/ReportsOutput/.
ESRI. 2002. ARCMap 8.3, Redlands, California.
Fortin, M., and M. Dale. 2005. Spatial Analysis - A guide for ecologists. Cambridge University
Press, Cambridge.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 214
Gray, J. S. 1989. Effects of environmental stress on species rich assemblages. Biological
Journal of the Linnean Society 37:19-32.
Hallgren, E., M. W. Palmer, and P. Milberg. 1999. Data diving with cross-validation: an
investigation of broad-scale gradients in Swedish weed communities. Journal of
Ecology 87:1037-1051.
Haury, J. 1996. Assessing functional typology involving water quality, physical features and
macrophytes in a Normandy river. Hydrobiologia 340:43-49.
Haury, J., M.-C. Peltre, S. Muller, M. Tremolieres, J. Barbe, A. Dutarte, and M. Guerlesquin.
1996. Des indices macrophytiques pour estimer la qualite des cours d'eau Francais:
premier propositions. Ecologie 27:233-244.
Hawkes, H. A. 1998. Origin and development of the biological monitoring working party score
system. Water Research 32:964-968.
Hill, M. O., D. B. Roy, J. O. Mountford, and R. G. H. Bunce. 2000. Extending Ellenberg's
indicator values to a new area: an algorithmic approach. Journal of Applied Ecology
37.
Holmes, N. T. H., J. R. Newman, F. H. Dawson, S. Chadd, K. J. Rouen, and L. Sharp. 1999.
Mean trophic rank: a users manual. R&D Technical Report, Environment Agency,
Bristol.
Irvine, K., R. Boelens, J. Fitzsimmons, A. Kemp, and P. Johnston. 2002. Review of the
monitoring and research to meet the needs of the EU Water Framework Directive.
2000-DS-5-M1, Irish Environmental Protection Agency, Johnstown Castle, Ireland.
Johnson, R. K. 2001. Indicator metrics and detection of impacts. Temanord: Nordic Council
of Ministers.
Kallis, G., and D. Butler. 2001. The EU water framework directive: measures and
implications. Water Policy 3:125-142.
Karr, J. R., and D. R. Dudley. 1981. Ecological Perspective on Water Quality Goals.
Environmental Management 5:55-68.
Kelly, M. G. 1998. Use of the trophic diatom index to monitor eutrophication in rivers. Water
Research 32:236-242.
Kelly, M. G., and B. A. Whitton. 1998. Biological monitoring of eutrophication in rivers.
Hydrobiologia 384:55-67.
Legendre, P., and M. J. Anderson. 1999. Distance-based redundancy analysis: testing
multispecies responses in multifactorial ecological experiments. Ecological
Monographs 69:1-24.
Legendre, P., and L. Legendre. 1998. Numerical Ecology. Elsevier Science, Amsterdam.
McElarney, Y. 2002. PhD Thesis: A comparison of classifications of reference lakes using
aquatic macrophytes and water body descriptors. University of Ulster, Coleraine.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 215
Milner, A. M., and M. W. Oswood. 2000. Urbanisation gradients in streams of Anchorage,
Alaska: a comparison of multivariate and multimetric approaches to classification.
Hydrobiologia 422/423:209-223.
Moss, B., D. Stephen, C. Alvarez, E. Becares, W. Van de Bund, S. E. Collings, E. Van Donk,
E. De Eyto, T. Feldmann, C. Fernandez-Alaez, M. Fernandez-Alaez, R. J. M.
Franken, F. Garcia-Criado, E. M. Gross, M. Gyllstrom, L. Hansson, K. Irvine, A.
Jarvalt, J. Jensen, E. Jeppesen, T. Kairesalo, R. Kornijow, T. Krause, H. Kunnap, A.
Laas, E. Lill, B. Lorens, H. Luup, M. R. Miracle, P. Noges, T. Noges, M. Nykanen, I.
Ott, W. Peczula, E. T. H. M. Peeters, G. Phillips, S. Romo, V. Russell, J. Salujoe, M.
Scheffer, K. Siewertsen, H. Smal, C. Tesch, H. Timm, L. Tuvikene, I. Tonno, T. Virro,
E. Vicente, and D. Wilson. 2003. The determination of ecological status in shallow
lakes - a tested system (ECOFRAME) for implementation of the European Water
Framework Directive. Aquatic Conservation: Marine and Freshwater Ecosystems
13:507-549.
Moss, D., M. T. Furse, J. F. Wright, and P. D. Armitage. 1987. The prediction of
macroinvertebrate fauna of unpolluted running-water sites in Great Britain using
environmental data. Freshwater Biology 17:41-52.
Murphy, K. J. 2002. Plant communities and plant diversity in softwater lakes of northern
Europe. Aquatic Botany 73:287-324.
Murphy, K. J., and M. M. Ali. 1998. Can functional groups improve on species assemblage
as the basis of indicator schemes for trophic assessment of rivers? Bulletin of the
British Ecological Society 29:20.
Nichols, S., S. Weber, and B. Shaw. 2000. A proposed aquatic plant community biotic index
for Wisconsin lakes. Environmental Management 26:491-502.
O'Conner, R. J., T. E. Walls, and R. M. Hughes. 2000. Using multiple taxonomic groups to
index the ecological condition of lakes. Environmental Monitoring and Assessment
61:207-228.
O'Connor, M. 2002. Mutual Information and Regression Maximisation (MIR-max) software
Version 0.2. e-mail: [email protected], Staffordshire University.
Palmer, M. A., S. L. Bell, and I. Butterfield. 1992. A botanical classification of standing waters
in Britain: application for conservation and monitoring. Aquatic Conservation: Marine
and Freshwater Ecosystems 2:125-143.
Palmer, M. A., and D. B. Roy. 2001. A method for estimating the extent of standing fresh
waters of different trophic states in Great Britain. Aquatic Conservation: Marine and
Freshwater Ecosystems 11:199-216.
Peters, R. H. 1981. Phosphorous availability in Lake Memphremagog and its tributaries.
Limnological Oceanography 26:1150-1161.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 216
Peters, R. H. 1986. The role of prediction in limnology. Limnological Oceanography 31:1143-
1159.
Polls, I. 1994. How people in the regulated community view biological integrity. Journal of the
North American Benthological Society 13:598-604.
Poole, G. C. 2002. Fluvial landscape ecology: addressing uniqueness within the river
discontinuum. Freshwater Biology 47:641-660.
Prairie, Y. T. 1996. Evaluating the predictive power of regression models. Canadian Journal
of Fisheries and Aquatic Sciences 53:490-492.
Racca, J. M. J., and Y. T. Prairie. 2004. Apparent and real bias in numerical transfer
functions in palealimnology. Journal of Paleolimnology 31:117-124.
Rejewski, M. 1981. Dissertation: Vegetation of lakes located in the Laski region in the
Tuchola Forests (in Polish). Nicolas Copernicus University, Turun.
Rejewski, M., and H. Ciecierska. 2004. Lake ecological state by the macrophyte method
(MPhI). Aquatic Botany:(prepared for press).
Simon, T. P., and J. Lyons. 1995. Application of the index of biotic integrity to evaluate water
resource integrity in freshwater ecosystems. in W. S. Davis and T. P. Simon, editors.
Biological Assessment and Criteria. Tools for water resource planning and decision
making. Lewis Publishers, New York.
Smith, B. D., P. S. Maitland, and S. M. Pennock. 1987. A comparative study of water level
regimes and littoral benthic communities in Scottish lochs. Biological Conservation
39:291-316.
Søndergaard M., E. Jeppesen, J.P. Jensen and S.L. Amsinck (2005) Water Framework
Directive: ecological classification of Danish lakes. Journal of Applied Ecology,
42:616-629.
SPSS Inc. 1999. Statistical Package for Social Scientists 9.0.1, Illinois.
Suter, G. W. 1993. A critique of ecosystem health concepts and indexes. Environmental
Toxicology and Chemistry 12:1533-1539.
Szoszkiewicz, K., T. Ferreira, T. Korte, A. Baattrup-Perdersen, J. Davy-Bowker, and M.
O'Hare. accepted for publication 2006. European river plant communities: the
importance of organic pollution and the usefulness of existing macrophyte metrics.
Hydrobiologia.
Taylor, D., M. Leira, C. Dalton, P. Jordan, K. Irvine, H. Bennion, and E. Magee. 2005. IN-
SIGHT. EPA/ERTDI Project: 2002-W-LS/7 Work Package 2, Dept. of Geography,
Trinity College, Dublin.
ter Braak, C. J. F. 1987. Unimodal models to relate species to environment. Agricultural
Mathematics Group, Wageningen, The Netherlands.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 217
ter Braak, C. J. F., and P. Smilauer. 2002. CANOCO version 4.5, Microcomputer Power.
Ithica, NY, USA.
ter Braak, C. J. F., and P. Šmilauer. 2002. CANOCO Reference Manual and CanoDraw for
Windows User's guide: Software for Canonical Community Ordination (version 4.5).
Microcomputer Power, Ithaca, NY USA.
ter Braak, C. J. F., and P. F. M. Verdonschot. 1995. Canonical correspondence analysis and
related multivariate methods in aquatic ecology. Aquatic Sciences 57:255-289.
Thiebaut, G., and S. Muller. 1998. The impact of eutrophication on aquatic macrophyte
diversity in weakly mineralized streams in the Northern Vosges mountains (NE
France). Biodiversity and Conservation 7:1051-1068.
Townsend, C. R. 1989. The patch dynamics concept of stream community ecology. Journal
of the North American Benthological Society 8:36-50.
Trigg, D., and W. J. Walley. 2002. Bayesian Belief Network Creator Software. Staffordshire
University.
Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, and C. E. Cushing. 1980. The
river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences
37:130-137.
Wetzel, R. G. 2005. Limnology - Lake and River Ecosystems. Academic Press (London)
imprint of Elsevier Science.
Willby, N. J., V. J. Abernethy, and B. O. L. Demars. 2000. Attribute-based classification of
European hydrophytes and its relationship to habitat utilisation. Freshwater Biology
43:43-74.
Wright, J. F. 1995. Development and use of a system for predicting the macroinvertebrate
fauna of flowing waters. Australian Journal of Ecology 20:181-197.
Wright, J.F. 2000. An introduction to RIVPACS. in Wright J.F., Sutcliffe D.W. and Furse M.T.
(Eds). Assessing the biological quality of fresh waters - RIVPACS and other
techniques. Freshwater Biological Association, Ambleside, Cumbria.
Wright, J. F., D. Moss, P. D. Armitage, and M. T. Furse. 1984. A preliminary classification of
running-water sites in Great Britain based on macro-invertebrate species and the
prediction of community type using environmental data. Freshwater Biology 14:221-
256.
Yoder, C. O., and E. T. Rankin. 1995. Biological response signatures and the area of
degradation value: new tools for interpreting multimetric data. in W. S. Davis and T.
P. Simon, editors. Biological assessment and criteria. Tools for water resource
planning and decision making. Lewis Publishers, Boca Raton, Florida.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 218
Yoder, C. O., and E. T. Rankin. 1998. The role of biological indicators in a state water quality
management process. Journal of Environmental Monitoring and Assessment 51:61-
88.
Yodzis, P. 1986. Competition, Mortality, and Community Structure. Pages 480 - 491 in J.
Diamond and T. J. Case, editors. Community Ecology. Harper & Row Publishers Inc,
New York.
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 219
Additional Recent Developments The development of methods to assess ecological quality should be seen as an ongoing
process rather than a finite exercise. Although the CBAS method has been validated and
shown to be very effective at predicting and separating different impacts, even a 95%
success rate in estimating EQR would result in 5% of sites having incorrect EQR values (15
sites in a network of 300 sites). Therefore feedback from operational monitoring throughout
the monitoring network is essential to enable the system to operate effectively at all sites.
Recommendations for future work within the EPA/EHS:
1. Further refine reference conditions Investigate any apparent discrepancies in impact or EQR between those indicated by CBAS
and what seems apparent to experts. This may be particularly apparent where the site is
quite unique and doesn’t represent the characteristic habitat at a particular alkalinity, slope
(rivers) or depth (lakes). Reference conditions for these sites may be altered based on expert
opinion. It is not necessary to change the expected species list, but only to suggest expected
metric values.
2. Improve allocation of status boundaries
The status boundaries suggested by Member States, despite the normative definitions within
the WFD, are still quite arbitrary. Even if they are reasoned to have boundaries for
ecologically specific reasons, inter-calibration will change these boundaries. Systematic
recording of what are judged to be high, good, moderate, poor and bad status whilst doing
field work could help to calibrate status boundaries to make them reflect a general
consensus on status classes (although monitoring systems are likely to detect impacts not
detectable by experts at many sites).
3. Feedback on the monitoring procedures and ease of use of CBAS
Critical feedback following the extensive use of fieldwork methods and CBAS would help to
streamline the methods, make them more user-friendly, and less prone to user error.
Further research work on index development could be:
1. More specific river and lake typology definitions to improve reference condition
prediction. Potentially hydromorphological and macrophyte typologies could be better
integrated.
2. Improve integration of different elements
NS Share project River and Lake Macrophytes – Index Development
(T1-A4-1.0) 220
Examination of the information provided by physico-chemical, hydromorphological and other
biological elements such that the overall EQR calculation does not contain excessive
redundancy between elements. This is related to (1) in that if reference conditions could be
better defined by specific hydromorphological forms (riffles/pools etc) better species
prediction could occur, although the hydromorphological assessment would be required to
ensure that the pool/riffle structure is at reference state.
3. Improvements in error determination The current attempts within Europe to estimate error (e.g. confidence of class) contain
assumptions which are unlikely to be true. For example, that error is consistent at different
EQR values, or that the confidence of a site belonging to each status class adds up to 100%.
Error may be most accurately determined by examining annual variation at sites judged to
have had no increase in impact (allowing estimation of natural variation).
4. Feedback to TAGs
It is evident that the structure of the WFD inhibits the ability to optimise the quantitative and
diagnostic assessment of impacts. It also distracts from the more accurate ‘state-change’
assessments which could be possible. Suggestions to improve the structure of the WFD may
permit future amendments.
5. Improve reference condition prediction method The CBAS approach modeling seems very effective, but the ability to predict reference
conditions may be much more variable from site to site. Therefore either an improved
reference condition prediction method is required, or ideally, site specific reference
conditions (using expert opinion and more information) are generated.
The current rivers and lakes methods reports have also incorporated changes to the
ecological status bands due to feedback from field work and to match the invertebrate
ecological status bands.
The science behind ecological monitoring is still in its early stages. From research carried out
over the last 6 years it is evident that a rush to make subjective decisions about ecological
processes and responses can lead to the perpetuation of inaccurate methods and possible
incongruity between ecological status as measured in different Member States. Although
pragmatic decisions do need to be made, it is important to distinguish guesswork from
scientifically supportable evidence, such that the areas of guesswork can be later amended
in light of scientific investigation. The variety of methods adopted within Europe and
difficulties with inter-calibration and error estimates suggest that there should be several
years of comparison, reflection and discussion before we can be sure that an optimal
approach to ecological status assessment has been achieved.