North South Shared Aquatic Resource (NS Share)...lakes. The work was funded through the North South...

North South Shared Aquatic Resource (NS Share) River and Lake Macrophytes

Index Development

NS Share project River and Lake Macrophytes – Index Development

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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


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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.


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Index Development River and Lake Macrophytes

Ian Dodkins and Brian Rippey

University of Ulster, Coleraine

N. Ireland


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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


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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


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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


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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


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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


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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


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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.


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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


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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.


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(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:


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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


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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).


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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.


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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

}


(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


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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).


(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).


(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.


(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


(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.


(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


(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


(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:


(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.


(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:


(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.


(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.


(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


(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


(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


(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.


(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.


(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


(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


(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


(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.


(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.


(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.


(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


(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.


(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


(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


(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.


(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.


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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.


(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.


(T1-A4-1.0) 47

RIVERS


(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.


(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.


(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


(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 : - - - -






eigenvalues 1


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






eigenvalues 13.160



(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


(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.


(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.


(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).


(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


(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.


(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


(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.


(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).


(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


(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


(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.


(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;


(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


(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.


(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


(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.


(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.


(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


(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.


(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


(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.


(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


(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


(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


(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


(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.


(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)


(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).


(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.


(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


(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


(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.


(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.


(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



(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.


(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



(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



(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.


(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).


(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.


(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.


(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


(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 *


(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


(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).


(T1-A4-1.0) 98

SUBS

NO3 PH


(T1-A4-1.0) 99

DO

NH4

WIDTH


(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


(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.


(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


(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


(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


(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


(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


(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.


(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


(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


(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


(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.


(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


(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.


(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.


(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.


(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.


(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


(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


Schoenoplectus lacustris

Scirpus fluitans

Scirpus maritimus

Sium latifolium


(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


(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


Schoenoplectus lacustris

Scirpus fluitans

Scirpus maritimus

Scrophularia aquatica

Senecio aquaticus

Sium latifolium

Sparganium (undecided)


(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).


(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.”


(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.


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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.


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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)


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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


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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


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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.


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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).


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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


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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.


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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.


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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.


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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


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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.


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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.


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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’


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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


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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).


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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).


(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


(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


(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


(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


(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


(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


(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


(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


(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


(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.


(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.


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.


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


(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


(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


(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


(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).


(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


(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.


(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


(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).


(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


(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


(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.


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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.


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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 ? - -


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LAKES


(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


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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.


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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


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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.


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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]


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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?]


(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).


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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).


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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


(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


(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


(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.


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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


(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.


(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.


(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


(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


(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


(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


(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.


(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


(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


(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.


(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.


(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


(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 *


(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.


(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


(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


(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


(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).


(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


(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)


(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


(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).


(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


(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).


(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


(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.


(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).


(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


(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


(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):


(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


(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.


(T1-A4-1.0) 211

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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


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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.

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