Hydrobiologia 516: 251–268, 2004.D. Hering, P.F.M. Verdonschot, O. Moog & L. Sandin (eds), Integrated Assessment of Running Waters in Europe.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

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A stressor specific multimetric approach for monitoring runningwaters in Austria using benthic macro-invertebrates

Thomas Ofenböck1, Otto Moog1, Jeroen Gerritsen2 & Michael Barbour2

1University of Natural Resources and Applied Life Sciences, Institute of Water Provision,Water Ecology and Waste Management; Department of Hydrobiology, Vienna, AustriaFax: + 43 (0) 1 47654 5217. E-mail: thomas.ofenboeck@boku.ac.at2 Tetra Tech, Inc., 10045 Red Run Blvd., Owings Mills, MD 21117, U.S.A.

Key words: Austria, benthic macro-invertebrates, metrics, multi-metric index, running waters, water frameworkdirective

Abstract

We investigated four stream types in four different bioregions, classified by catchment area and altitude, andstressed by different degrees of organic pollution and habitat alteration. We examined a macro-invertebrate basedmultimetric approach for Austrian rivers as a potential assessment method within the European Water FrameworkDirective. Benthic macro-invertebrate data (100 samples including reference sites) were used to develop a mul-timetric index for each stream type and targeted stressors. Sites were pre-classified based on physical, chemical,and land use criteria into five ecological quality classes. More than 200 biological metrics were tested for theirsensitivity to the targeted stressors, their spatial and temporal variability and their ability to discriminate betweendifferent types and degrees of stress. Metrics for index development were selected to reflect different levels ofinformation including ecosystem, community, and individual levels (Karr, 1991; Barbour et al., 1995; Gerritsen,1995). Combinations of metrics were selected to distinguish best between non or slightly impaired and stressedsites (evaluated by calculating discrimination efficiency values and power analysis). The resulting four indicescomprised seven to nine metrics from five to seven metric categories, and distinguished reference/slightly dis-turbed sites from stressed sites with close to 100% efficiency. The indices can form the basis for stressor-specificassessment of stream condition.

Introduction

The European Water Framework Directive (WFD) setsuniform standards in water management throughoutthe European Union by stipulating the developmentof integrated and coordinated river basin managementplans for all European water bodies (Council of theEuropean Union, 2000). Rivers, lakes, groundwaterand marine water bodies are seen and managed asecosystems by a single legislative framework. Theenvironmental objectives of the WFD are to preventfurther deterioration and to protect and enhance thestatus of aquatic ecosystems, explicitly to maintainor achieve at least ‘good ecological status’ for allsurface waters by 2015. The determination of the ‘eco-logical status’ is based on characterizing reference

conditions for water bodies that are not heavily mod-ified. A main focus of the WFD is the use of bioticelements in stream assessment, namely algae, macro-phytes, benthic invertebrates and fish. To describe thebiological elements the following attributes have to beconsidered: composition, abundance, the ratio of dis-turbance sensitive taxa to insensitive taxa. The WFDenforces a re-orientation of monitoring procedurestowards an integrative approach.

Instead of documenting the biological water qual-ity with respect to organic pollution, the WFD as-sessment of ecological status of water bodies con-siders the relationships between biota and the hydro-morphological and chemical components.

This paper summarizes methods and results ofdeveloping an integrated monitoring approach based

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Figure 1. Location of aquatic bioregions / stream types investigated in AQEM. (Aquatic ecoregions (Moog et al. 2001a): IV = Alps, IX = Cent-ral European Mountains, X = Carpathians, XI = Hungarian Plains; aquatic bioregions (Moog et al. this issue): A01 = Eastern Ridges &Lowlands A02 = Limestone Foothills, A03 = Non-Glaciated Alps, A04 = Austrian Granite and Gneiss Region).

on benthic invertebrates. It follows a multimetricprocedure as developed within the AQEM researchproject (‘The Development and Testing of an Integ-rated Assessment System for the Ecological Qual-ity of Streams and Rivers throughout Europe usingBenthic Macroinvertebrates’, Contract No.: EVK1-CT 1999-00027) carried out under the 5th FrameworkProgramme of the European Union. Compareable sys-tems have recently been developed in the neighbouringcountries (Brabec et al., 2004; Buffagni et al., 2004;Lorenz et al., 2004; Vlek et al., 2004).

Successful biological monitoring depends on pre-cise measures of a site’s fauna or flora, especiallythose components that are influenced most by perturb-ation. Thus, the spatial and temporal scale of samplingshould detect and foster understanding of human in-fluences, not document the magnitude and sources ofnatural seasonal or successional variation in the samesystem (Karr & Chu, 1999).

The AQEM assessment method applied in Austriais based on a ‘multimetric’ procedure. A multimetricindex combines several individual biotic metrics (e.g.saprobic index, feeding type composition), which arecombined into a unitless multimetric index. Thus,multimetric indices integrate multiple attributes ofstream communities to describe and evaluate a site’s

condition. Aggregation of metric scores simplifiesmanagement and decision making so that a singleindex value is used to determine whether action isneeded. (Karr et al., 1986). Metrics are defined as‘Measurable parts or processes of a biological systemempirically shown to change in value along a gradientof human influence’ (Karr & Chu, 1999). Useful met-rics are:

− ecologically relevant to the biological assemblageor community under study and to the specifiedprogram objectives and

− sensitive to stressors and provide a response thatcan be discriminated from natural variation.

The goal of the project was to develop and test anassessment system to characterise the biological con-dition of sites, where biological degradation is strictlydue to human stressors. A requirement for developingsuch a system is that river stressors must be estimatedobjectively without using biotic information to avoidcircular statements. A main design criteria for this pro-ject was to identify stressor-specific responses of thebiological metrics.

Our approach was to identify and sample a gradientof stressors in each of 4 major Austrian bioregions (re-gions with characteristic aquatic fauna composition).

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Each bioregion had a separate stressor gradient. Po-tential metrics were tested for each bioregion, anda separate index was developed for each bioregion.Each index is the average of selected responsive, non-redundant metrics. Because of small sample size (totalof 100 sites among the 4 bioregions), the indicesmust be viewed as preliminary and subject to furtherrefinement.

Investigated stream types

The sites investigated in Austria in the context ofAQEM are distributed over four different bioregions(Moog et al., 2004) within 3 ecoregions (Illies, 1978;Moog et al., 2001a) (Fig. 1 and Table 1).

The reference (undisturbed) status of the 4 differ-ent stream types is briefly characterised as follows:

A01 Mid-sized streams in the Hungarian PlainsMid-sized streams in the Hungarian Plains are low-gradient, and usually sinuate or meandering within abroad floodplain. Undercuts and slip slopes are fre-quent due to erosion power. Sediments are dominatedby sand and silt; gravel and cobble are limited toriffles. There is a large amount of large woody debrisproviding habitats for invertebrate filter-feeders andgrazers. Dense woody bank vegetation and a dynamichydrological regime foster intact land-water transitionzones. Oxbow lakes and other standing water bod-ies are still present. These streams are up to 15 mwide, with average current velocity of about 0.4 m s−1

and mean depth between 20 and 50 cm. Specific con-ductance under reference conditions ranges from 150to 220 µS cm−1; total hardness ranges from 0.6 to0.8 mmol l−1. The discharge regime shows eithersummer peaking with low values in winter or max-ima between March and April with additional floodsthroughout the year.

The macroinvertebrate community is dominated bypotamophilic species. Detritivorous feeders are mostfrequent, as well as filter-feeders (e.g., Trichoptera likeHydropsyche-species, Brachycentrus subnubilus andfreshwater mussels) and grazers (e.g., Ephemeropteralike Ecdyonurus, Rhithrogena and the xylobiont beetleMacronychus quadrituberculatus).

This stream type is threatened by channelization(straightening), river bed incision and damming incombination with the removal of natural floodplainvegetation for agricultural uses. In addition, pollutionfrom effluent as well as diffuse inputs of nutrients andtoxic substances due to agricultural land use result in

devastated water channels, lacking the sensitive taxamentioned above.

A02 Mid-sized calcareous pre-alpine streamsThis stream type consists of mostly high gradientstreams that flow in V- to U-shaped valleys and haveboulders, blocks, bedrock and cobbles as domin-ant substrates. Gravel and sand is deposited nearthe shoreline as well as in pools. Streams are up to30 m wide, the mean current velocity is about 0.5to 0.7 m s−1, and mean depth varies between 20and 50 cm. Due to geology the specific conductanceis relatively high (250 to 400 µS cm−1) under ref-erence conditions. Total hardness varies from 1.6 to1.8 mmol l−1. The background concentration of totalphosphorous does not exceed 10 µg l−1.

Diatoms and mosses colonise the stony substratedepending on the amount of light, which is oftenlimited by dense native deciduous/coniferous forest.Wetland areas are rare, although spring fed tribu-taries occur frequently along the valley bottom andcan contribute significantly to taxa richness. The dis-charge regime is influenced by snow melt in spring(pluvio-nival regime) with highest discharges occur-ring between April and May.

A high proportion of rheophilic EPT taxa (Eph-emeroptera, Plecoptera, Trichoptera) is character-istic of the macroinvertebrate community. Shreddersand grazers are well represented, e.g., Limnephil-idae (Trichoptera), Taeniopterygidae, Protonemura,Nemoura, Leuctra (Plecoptera), Rhithrogena, Ecdyon-urus, Epeorus, Baetis (Ephemeroptera), Hapalothrixand Liponeura (Diptera).

The stream type is widely distributed within thealpine area in Austria. Habitat alterations are mainlystraightening, bank fixation, paving, retention of bedload and impounding. Damming and water diversionfor power generation cause severe ecological problemswithin vast stretches of residual flow often combinedwith pulse releases. Organic pollution, although evid-ent in regions of tourism (skiing, mountaineering),represents a minor problem.

A03 Small non-glaciated crystalline alpine streamsThe stream type consists of high gradient streamsflowing in V- to U-shaped valleys. Extensive pool-riffle sequences are characteristic of these natur-ally constrained brooks. The bed consists mainly ofboulders, blocks, bedrock and cobbles. In current-reduced zones, gravel and sand is deposited. Diatomsand mosses colonise the stony substrate but may be

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Table 1. Attributes of investigated stream types and targeted stressors

Stream type / bioregion Size class Altitude class Ecoregion Investigated

(m.a.s.l.) pressures

A01 Mid-sized streams in the >100–1000 km2 200–800 Hungarian Plains Organic pollution

Hungarian Plains / Eastern

Ridges and Lowlands

A02 Mid-sized calcareous streams / >100-1000 km2 200-800 Alps Morphology:

Limestone Foothills channel alteration

A03 Small non-glaciated crystalline 10–100 km2 200–800 Alps Morphology:

streams / Non- channel alteration

Glaciated Central Alps

A04 Mid-sized streams in the >100–1000 km2 200–800 Central European Impoundment

Bohemian Massif / Austrian Mountains

Granite and Gneiss Region

limited by dense native deciduous/coniferous forest.Debris dams are typical due to the high autumnal inputof particulate organic matter. Wetland areas are rare,except some spring fed tributaries along the valley bot-tom. Stream width can reach up to 10 m; the meancurrent velocity is about 0.3 to 0.5 m s−1, and meandepth varies between 10 and 30 cm. Specific conduct-ance is relatively low with values ranging between 30and 200 µS cm−1, and total hardness varies from 0.3to 0.8 mmol l−1 under reference conditions. Dischargeis characterised by spates in June (nival regime).

As in the calcareous alpine streams, a high di-versity of EPT taxa is characteristic of the macroin-vertebrate community. Shredders and grazers are oftendominant.

This stream type is widely distributed within thealpine area in Austria. Alterations are also similar tothe calcareous alpines streams: stream regulation, suchas damming and water diversion and minor organicpollution.

A04 Mid-sized streams in the Bohemian MassifThis stream type comprises low to middle gradientstreams flowing through U-shaped valleys. The chan-nel form is mostly sinuate, except in low gradientsections, where meanders occur. The composition ofstreambed substrates is diverse and characterised bylarge blocks and relatively high proportions of sand,especially in low gradient zones and downstream ofboulders. Bed load transport is very low in compar-

ison to alpine streams. Organic habitats are common,in particular mosses, debris dams and logs.

Stony substrates are colonised by diatoms and/ormosses depending on the amount of light, which isoften limited by deciduous or mixed native forest,dominated by beech (Fagus sylvatica). The high per-centage of moss-covered boulders reflects stable sub-stratum conditions. The average stream depth is about0.5 m, the average width between 10–15 m. Meandischarge is about 2500–3500 l s−1 and temperatureexceeds 20 ◦C in summer. Connected standing waterbodies in the floodplain are rare and primarily re-stricted to short meandering stretches. Discharge ischaracterised by spates between April and May dueto snow melting (pluvio-nival regime).

In Austria the Bohemian Massif mainly consists ofgranites and gneisses, which are low in calcium. Thiscauses soft waters characterised by low total hard-ness (0.3–0.4 mmol l−1) and low specific conductance(100–130 µS cm−1) and pH values between 6.9–7.9under reference conditions. Floods – especially duringsnowmelt periods – can lead temporarily to lower pHvalues.

A large number of macrobenthic families is typicaland reflects the heterogeneity of substrates and mi-crohabitats, while the number of species is relativelysmall in relation to comparable streams in limestoneareas. The number and abundance of Elmidae spe-cies, which preferably colonise mosses and livingroots, are high, as well as EPT taxa. High pro-

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portions of shredders and grazers, e.g., Limnephil-idae (Trichoptera), Taeniopterygidae, Protonemura,Nemoura and Leuctra (Plecoptera), Baetis, Rhithro-gena, Ecdyonurus, Epeorus, Ephemerellidae (Ephem-eroptera), Blephariceridae (Diptera) and Gammarus(Amphipoda) are frequently found. Other typical spe-cies of this region are e.g. Hydropsyche silvfenii,Micrasema longulum, Anomalopterygella chauvini-ana and Capnopsis schilleri. Ephemera danica isfound in high densities in sandy substrates, Baetissp. and Ephemerella sp. frequently occur in mossesand stony substrates as compared to Heptageniidae. InAustria, the highly endangered species Margaritiferamargaritifera (Unionidae, Mollusca) occurs only inthe Bohemian Massif.

This stream type is restricted to the northern partof Upper and Lower Austria. Beside organic pollu-tion and impairment of stream morphology, the mostimportant degradation is the increasing amount of un-stable sandy substrates mainly due to drainage meas-ures in (unnatural) coniferous forests. Acidificationis assumed for some headwater reaches. Due to lowpopulation density in this part of Austria, near-naturalstretches can still be found.

Materials and methods

AQEM data base

The AQEM database contains taxa lists of themacroinvertebrate fauna and data on river morpho-logy, physico-chemistry and catchment characterist-ics of the individual sites. Standardised methods forsampling design, sample treatment, taxonomic resolu-tion and data processing were applied as described inHering et al. (2004). Two samples in different seasonswere sampled per site using a multihabitat approach(Barbour et al., 1996; Hering et al., 2004). The AQEMsampling method is based on a multi-habitat schemedesigned to sample major habitats with more than5% coverage in proportional representation within asampling reach. Macroinvertebrates are collected sys-tematically from all available instream habitats. Atotal of 20 ‘sampling units’ is taken from all majorhabitat types in the reach resulting in sampling of ap-proximately 1.25 m2 of habitat. For example, if thehabitat in the sampling reach is 50% sand, then 50%or 10 sample units should be taken in that habitat.Animals were identified at the species level wherepossible.

Pre-classification of sites according to theirdegradation (based on the AQEM site protocol data)

Reference sites were distinguished from impairedsites, but an exact differentiation between all gradu-ations of stream quality classes could not be donebeforehand. The WFD quality classes were adopted (5= reference (high), 4 = good, 3 = moderate, 2 = poor, 1= bad), and defined for gradients of impoundment andstream morphology as follows:

Impoundment5 No impoundment.4 Natural bed sediments but reduced current condi-

tions (e.g., headrace sections).3 Bed sediments with smaller grain sizes, flow velo-

cities diminished.2 Sediment conditions and flow velocities remark-

ably different from natural conditions.1 Bed sediments very different from natural condi-

tions, in many cases consisting of untypical finesediments (mud), nearly no current.

Stream morphology degradation5 Channel patterns, width and depth variations, flow

velocities, substrate conditions and both the struc-ture and condition of the riparian zones correspondtotally or nearly totally to undisturbed conditions.Ecomorphological Quality Class I (Ecomorpholo-gical Quality Classes according to Werth, 1987 andSpiegler et al., 1989).

4 Ecomorphological Quality Class II, with respect toriparian zones II-III.

3 Ecomorphological Quality Class II-III (exclusiveof riparian zones) and III (exclusive of flow velo-cities and substrate conditions: II-III only).

2 Ecomorphological Quality Class III-IV respect-ively III with respect to flow velocities and sub-strate conditions.

1 Channel patterns, width and depth variations, flowvelocities, substrate conditions and both the struc-ture and condition of the riparian zones completelydisrupted. Ecomorphological Quality Class IV andIII-IV with respect to flow velocities and substrateconditions.

A pre-classification was carried out based on avail-able information (maps, public authorities etc.) andexpert judgement on the site. In a second step, relevantdata on abiotic characteristics of the sites were collec-ted and evaluated to verify the pre-classified qualityclasses (final pre-classification).

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Table 2. Distribution of samples withinpre-classified quality classes

Ecological Stream type

quality class A01 A02 A03 A04

Reference (high) 8 6 8 6

Good 4 8 6 6

Moderate 4 4 6 6

Poor 6 5 4 4

Bad 2 3 2 2

The selection of appropriate variables to measurethe degradation of the sites depends on stream typeand stressor. The following parameters have proven tobe significant in Austria and were used for the finalpre-classification:

• classification of site’s degradation caused by or-ganic pollution:– log10 total phosphorus content,– conductance, total hardness, alkalinity,– content of nitrate, nitrite, ammonium, total

phosphorus and BOD5,– results of Austrian Field Assessment scheme

(Moog et al., 1999),• classification of site’s degradation caused by

stream morphology degradation and impound-ment, respectively:– ecomorphological evaluation according to Werth

(1987) and Spiegler et al. (1989) evaluating:– degree of bank- and bed fixation– composition and distribution of bed sediments

(proportion of most frequent substrate, numberof different substrate types)

– composition of riparian vegetation– variances of width, depth and current– presence/absence of coarse woody debris

The distribution of samples within the different classesis shown in Table 2.

Calculation of metrics

Starting from the biological data (taxa lists, abund-ance) a large number of biological metrics were cal-culated to identify those metrics that are stronglyassociated with the parameters selected for the pre-classifications of sites. Most of the metrics were cal-culated based on the software package ECOPROF 2.0(Moog et al., 2001b), for some additional metrics the

AQEM Excel macro ATIC was applied. We used boxand whisker-plots to visualize metrics values for dis-crimination between reference and stressed sites. Forstatistical analysis and graphical visualisation the soft-ware package STATISTICA 5.5 (StatSoft. Inc., 2000)was used.

Identification of candidate metrics

All metrics that represent ecologically relevant as-pects of the assemblage and respond to the targetedstressors are potential metrics for a final index. Out ofthe ‘universe’ of metrics, some have to be eliminatedbecause of insufficient data or because the range ofvalues is not adequate to discriminate between naturalvariability and anthropogenic effects.

The potential use for each metric is based on pos-sessing enough information and a specific range ofvariability to discriminate among site classes and eco-logical quality (Barbour et al. 1999). Metrics thatcontained too many zero values within the referencesites were excluded to ensure that metrics are calcu-lated at a large enough proportion of sites. Descriptivestatistics were used (central tendency, range, distri-bution, outliers) to characterize metric performancewithin the population of reference sites of each siteclass. Metrics that could not discriminate sufficientlyamong sites of different condition were eliminated.The remaining metrics, which were used in furtheranalysis, are termed as candidate metrics.

Selection of robust measures (core metrics)

Core metrics are those that clearly discriminatebetween good and poor quality of ecological con-ditions. The testing of candidate metrics and theselection of core metrics includes (Barbour et al.,1999):• test of seasonal and spatial stability• independence on non-stressor-gradients• discriminatory power to indicate stress• removal of redundant metrics

Assessing variability in reference conditions

The WFD requires a ‘sufficient level of confidenceabout the values for the reference conditions’, as wellas adequate confidence and precision in the classi-fication. It is up to the Member States to define thelevel of confidence, taking into account the naturalspatial and temporal variability together with errorsassociated with sampling and analysis.

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Methods for establishing reference condition andsetting class boundaries must include an estimate oferror (EC REFCOND Working Group, 2001). Thisinformation is needed to determine the confidence andprecision in status classification. In this way, estim-ates of biological reference condition will incorporatethe natural variability of the quality element in timeand space, and the errors in the method of estimation.Sources of uncertainty in the observed biological qual-ity fall roughly into the following categories:

• Sampling errors (natural spatial variation). Thisis due to spatial heterogeneity in the microhabitatswithin each site/water body. Taxonomic richnessand composition will vary among samples takenduring the same period.

• Sample process errors. During sorting the materialin a macroinvertebrate sample and identification oftaxa, some taxa may be missed or misidentified.This may lead to underestimation for number oftaxa at the site.

• Natural temporal variation. The taxa present at asite will vary naturally over time.

Although the number of samples was restricted to atotal of 100, all candidate metrics were tested con-cerning their spatial and temporal stability within non-or slightly affected sites as well as within clearlyimpaired sites for each stream type. Those metricsdefined as core metrics showed low variability withinthese two groups and had only negligible overlap. Toevaluate temporal variability of metrics in a streamtype the mean of the coefficient of variance was cal-culated within each site (two seasons). For expressingspatial variation the mean of coefficients of vari-ance within all sites in both seasons were calculatedfollowing Sandin & Johnson (2000).

Removal of redundant metrics

Metrics that are highly correlated with each other andshow linear gradients contribute approximately thesame information, but those with scatter in the cor-relation or those showing nonlinear gradients wouldstill contribute useful information despite strong cor-relation (Karr et. al, 1986; Karr, 1991; Barbour et al.,1996). To avoid redundancy the pre-selected metricswithin metric types were tested using pairwise correl-ation analysis (Pearson product-moment correlation) .A correlation coefficient r = 0.75 was defined as theupper limit.

Transformation of metrics into unitless scores andindex calculation

Multimetric indices provide a means of integratinginformation from the various measures of biologicalmetrics. For the development of an integrated index,normalizing of core metrics via transformation to unit-less scores is essential. The standardization assumesthat each metric has the same value and importance.The method applied in this study follows the RapidBioassessment Protocols of the United States – En-vironmental Protection Agency (Barbour et al., 1999):The scoring criterion for each metric is based on thedistribution of values in all sites, including referencestreams. The 95th percentile of the data distribution ofa single metric is used to eliminate extreme outliers.From this upper percentile, the range of the metricvalues is standardized as the percentage of the 95thpercentile value to provide a range of scores. Valuesthat are close to the 95th percentile receive higherscores, values having a greater deviation from this per-centile have lower scores. Values that exceed the 95thpercentile are scored as 1. For those metrics values thatincrease in response to perturbation (‘reverse’ metrics,e.g., saprobic index or [%] Oligochaeta & Diptera)the 5th percentile is used to remove outliers and toform a basis for scoring. The resulting index valuesare calculated by simply averaging the score values.

Determination of the best aggregation of coremeasures for indicating status and change incondition

For aggregating metrics into multimetric indices spe-cial emphasis was given to cover as many differentmetrics types as possible and to select combinationsof metrics which show the best discriminatory powerto distinguish between non or slightly impaired andstressed sites. Furthermore, the metrics were selectedto include – if possible – at least all four primary met-rics types (Karr & Chu, 1999). To evaluate the strengthof the final index the discrimination efficiency (DE)values and Cohen’s d (Cohen, 1988) were estimated.Cohen’s d characterises the degree to which sampleresults diverge from the null hypothesis, and is definedas the difference between the means, M1−M2, dividedby standard deviation, s, of either group.

The DE was calculated as the percentage ofstressed samples with metric values lower than the25th percentile of reference values for decreasing met-rics, and higher than the 75th percentile for increasingmetrics, respectively (Fig. 2). Reference and good

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Figure 2. Discrimination efficiency. The discrimination efficiencyis expressed as the percentage of stressed sites showing valueslower than the 25th percentile of reference values for decreasingmetrics, and higher than the 75th percentile for increasing metrics,respectively.

sites were pooled and tested against stressed sitessince this is the crucial threshold on whether action isneeded to improve the quality of a river section withinthe WFD (European Union, 2000).

Definition of threshold values for ecological qualityclasses

The derivation of threshold values to discriminatebetween different stages of stress are based on the in-dex ranges. Different methods have been developedfor defining boundaries between quality classes (seeBarbour et al., 1999). Following the demands of theWFD, a five class scheme was used where the 25thpercentile of reference site distribution is fixed as thelower limit to separate reference sites from stressedsites.

The proposed class boundaries in the AQEM in-dices were defined using unequal class ranges con-sidering that not all of the involved metrics show alinear relationship to stressor intensity. The 25th per-centile of the site distribution of the higher class (usingpre-defined classes) was used to distinguish betweendifferent classes, however, if there was no overlapbetween two classes, the 5th percentile was used. Theappropriateness of the thresholds was verified with theindex performance (DE) and precision estimates.

Application of the indices: evaluating new streams

First, new streams are assigned to membership of astream type (A01, A02, A03, A04). Core metrics forthe targeted index are calculated and converted into

unitless scores. The resulting scores are averaged andused for rating and interpreting a site’s condition. Thecomponent metrics can be used to aid in determinationof cause and effect.

Results

Stressor specific response of metrics

Some metrics respond significantly to one type ofstressor but show no or sometimes even reverse re-sponse to another stressor. Table 3 gives an overviewof some frequently used metrics and shows their sens-itivity to the three targeted stressors. For example thelongitudinal zonation index is a very sensitive met-ric to identify changes caused by impoundment butit is inappropriate for indicating degradation inducedby channelization (Fig. 3). Another example is theproportion of passive filter feeders, which declinesin impounded river sections, but does not respond toorganic pollution. As shown in Table 3, the sensitiv-ity of frequently used metrics depends on the type ofstressor. Only metrics representing taxa richness anddiversity (e.g., EPT taxa, total number of taxa, in-tolerant taxa, diversity indices) show a unidirectionalresponse to all stressor types.

Some composition measures are not sensitive tochannelization or they may give results opposite tothose usually expected (Table 3). For example, chan-nelization (straightening) may favour animals livingon stony substrate (e.g., scrapers or clingers) whilesoft substrates may be missing or strongly reduced.These changes in habitat composition simulate up-stream conditions and can lead to confounding re-sponses, e.g., the proportion of shredders and grazers(RETI) (Schweder, 1992).

Candidate metrics

More than 200 potential metrics were tested for re-sponse to the identified stressors and to determinetheir potential for discriminating between referenceand stressed conditions in at least one of the AQEMstream types. The suitability of each metric was eval-uated by visualizing the distribution of metrics withinthe pre-classified quality classes. The discriminationefficiencies were calculated to evaluate the most suit-able measures. Finally 43 metrics were selected ascandidates. The highest number of candidates wasfound for stream type A04 (26), followed by streamtype A01 (18), A03 (15) and A02 (14) (see Table 4).

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Figure 3. Examples of two metrics (Longitudinal Zonation Index and [%] passive filter feeders) and their response to different stressors.

Figure 4. Examples of the distribution of core metrics values in different stream types showing high response to the targeted stressors.

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Figure 5. Box & whisker – plots of the resulting indices in context with the pre-classification.

Table 3. Examples of some commonly used metrics and their response to three different stressors. +. . . suitable,−. . . unsuitable

Metric Hungarian Plain Bohemian Massif Alpine streams

(A01) (A04) (A02, A03)

Organic pollution Impoundment Channel alteration

# Plecoptera taxa + + +# Trichoptera taxa − + +# EPT (EP) taxa + + +# total taxa + + +Saprobic index (Zelinka & Marvan, 1961) + + −Longitudinal zonation index + + −[%] Oligochaeta+Diptera Taxa + + −[%] EPT (EP) taxa + + −RETI ([%] shredder & grazer) + + −/

[%] of littoral+profundal preferences + + −/

# of sensitive taxa + + +Diversity (Margalef) + + +[%] active filterers, [%] passive filterers − + −[%] gatherers/collectors + + −

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Table 4. Candidate metrics for inclusion in the indices. BMWP and ASPT were calculated according to Armitage et al. (1983), RETI (shareof shredders+grazer) following Schweder (1992) and [%] pelal is calculated as percentage of the community preferring the microhabitat pelal(grain size < 0.063 mm). # abundance = total number of individuals, [%] abundance = relative abundance of individuals, # taxa = total numberof taxa, [%] taxa = relative number of taxa

Stream type

A01: Mid-sized streams in theHungarian Plains (n = 18)

A02: Mid-sized calcareousstreams (n = 14)

A03: Small non-glaciated crystal-line streams (n = 15)

A04: Mid-sized streams in theBohemian Massif (n = 26)

# EP taxa # Ephemeroptera taxa # EPT # taxa BMWP

# total families # Trichoptera # taxa # Total taxa Weighted ASPT

[%] EPT # taxa # Plecoptera # taxa Ratio Oligochaeta & Diptera/ Saprobic Index

[%] EP taxa # EPT # taxa EPT taxa [%] Littoral

[%] EP (abundance) # EP # taxa [%] Oligochaeta & Diptera taxa [%] Profundal

[%] Oligochaeta&Diptera taxa # total taxa Longitudinal zonation index

# Oligochaeta (abundance) [%] EPT [%] Passive Filter Feeders

# Plecoptera (abundance) [%] Oligochaeta&Dipterataxa

# Oligochaeta (abundance) [%] Gatherers+Collectors

Diversity (Margalef) ratio Oligochaeta&Diptera/ # Plecoptera (abundance) RETI

Saprobic Index EPT taxa Diversity (Margalef) [%] Pelal

BMWP # Plecoptera (abundance) BMWP # total individuals

ASPT # Trichoptera (abundance) # of sensitive taxa Diversity (Whilm & Dorris)

# of sensitive taxa [%] EP (abundance) RETI Diversity (Shannon & Wiener)

RETI Diversity (Margalef) [%] Active Filter Feeders # Orthocladiinae taxa

[%] Shredders ASPT [%] Gatherers+Collectors # Coleoptera taxa

[%] Gatherers+Collectors # of sensitive taxa [%] Littoral+Profundal # Ephemeroptera taxa

[%] Littoral+Profundal # Trichoptera taxa

longitudinal zonation index # Plecoptera taxa

[%] EPT

# Total taxa

# Total Families

# Diptera taxa

[%] Oligochaeta & Diptera taxa

# Ephemeroptera (abundance)

# Trichoptera (abundance)

[%] EPT (abundance)

Final selections of core metrics for aggregating amultimetric index of biological condition

Examples of the distribution of reliable core metricsare given in Figure 4. Metric aggregations that dis-criminated best between the different stages of disturb-ance are summarized in Table 5 including calculatedDE’s for metrics as well as for the final index.

Richness measures contribute to all four of themultimetric indices. Total taxa richness as well asEPT taxa (Ephemeroptera, Plecoptera, Trichoptera)richness were reliable measures showing consistentlyhigh discrimination efficiency and were included inthe index in three cases. In the mid sized streams ofthe Hungarian Plains (A01) the number of families

showed higher discrimination power than total taxa(lowest taxonomic level), and EP taxa richness wassubstituted for EPT taxa.

Absolute abundance measures are components ofeach index. Although the power to discriminate refer-ence sites from stressed sites with Oligochaeta abund-ance is low, it contributes to the index in stream typeA03 because it clearly separates strongly impactedsites.

Three different composition measures were selec-ted: [%] EPT taxa, showing high discriminatory powerin stream type A02 and [%] EP taxa in stream typeA01 whereas [%] Oligochaeta & Diptera taxa weremost powerful in stream types A03 and A04.

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Table 5. Metric types and core metrics implemented in the final indices and their discrimination efficiency to distin-guish between reference/slightly disturbed sites and stressed sites in different stream types. For the resulting indicesthe values for the statistical power and effect size (Cohen, 1988) are given (statistical power = reference sites andslightly disturbed sites to disturbed sites, statistical powerref = only reference sites to disturbed sites. Sample sizeis 24 (for stream types A01 and A04) and 26 (stream types A02 and A03) respectively. Sample breakdown amongclosses of stress shown in Table 2.

Metric type Metric DE

A01 A02 A03 A04

Richness measures # of total taxa 91.67 91.67 100

# of total families 83.33

# of EPT # taxa 75 100 91.67

# of EP # taxa 100

Abundance measures # of total individuals (abundance) 83.33

# of Plecoptera (abundance) 100 83.33

# of Oligochaeta (abundance) 50

# of Trichoptera (abundance) 83.33 91.67

[%] EP # individuals 91.67

Composition measures [%] EPT # taxa 100

[%] Oligochaeta & Diptera taxa 66.67 91.67

[%] EP # taxa 100

Tolerance/intolerance measures Saprobic index 100

# of sensitive taxa 100 83.33 91.67

Functional/Trophic measures RETI 75

[%] Gatherers+collectors 91.67

[%] Shredder 66.67

Zonation measures Longitudinal zonation index 100

Zonation measures/ [%] Littoral+profundal 58.33

Current Preference measures [%] Littoral 83.33

Diversity measures Diversity (Margalef) 50 66.67 91.67

Index: DE 100 83.33 91.67 100

Statistical power (%) 99.98 99.82 99.92 100

Effect size (Cohen’s d) 2.48 2.04 2.10 3.02

Statistical powerref (%) 99.97 99.97 99.97 100

Effect size (Cohen’s d) 2.61 2.01 0.93 1.93

Total number of metrics 9 7 8 8

Number of metrics types covered 6 5 7 5

Within tolerance/intolerance measures the percent-age of Austrian Sensitive Taxa (selected taxa con-sidered to be sensitive to impacts; Moog et al., 1999)showed very useful results in all stream types. Onlyin stream type A04 (Bohemian Massif) was this met-ric not included into the index, because there was toomuch overlap between good and moderate conditions.

Among functional/trophic measures, the percent-age of gatherers & collectors gave reliable results forstreams stressed by impoundment (stream type A04),

increasing clearly with increasing stress. Percentshredders showed good response to organic pollution(stream type A01) declining with increasing disturb-ance. The Rhithron Feeding Index RETI (Schweder,1992), a composite of [%] shredders and grazers, wasincluded in the index for stream type A03 stressed bychannel alteration (straightening). This metric is anexample of a measure which shows different reactionsdepending on the kind of stress. Channel straighten-ing increases the proportion of these feeding types,

263

but other stressors, e.g. organic pollution, reduce theRETI.

The Longitudinal Zonation Index (Moog et al.,2001b) was integrated in the index for stream type A04distinguishing clearly between all pre-classified ecolo-gical quality classes. The percentage of littoral & pro-fundal preferences (Moog, 1995) and the percentageof littoral preferences (representing measures of cur-rent preferences as well) are comprised in the indicesfor stream type A01 (Hungarian Plains) and A03(Non-Glaciated Central Alps), respectively. In case ofstream type A03 the power of [%] littoral & profundalpreferences to separate reference sites from stressedsites is low, but because it clearly discriminates higheffected sites from all other sites it was included to thefinal index.

Among diversity indices tested, the Margalef di-versity index (Margalef, 1958) gave the best resultsin all stream types. This measure contributes to theindices of all stream types except stream type A04.

Index

For stream type A01 (mid sized streams in theHungarian plains, stressor organic pollution) 9 met-rics were chosen, representing six different metrictypes. The DE’s vary between 67% ([%] shredders)and 100% for a total of five measures. The DEof the multimetric index is 100%, (Cohen’s d =2.48), which means that there is nearly no overlapbetween reference/slightly disturbed sites and stressedsites. Comparable discrimination efficiency was foundbetween all other pre-defined quality classes. Thehuman impairment in stream type A02 (mid-sizedcalcareous streams in the Limestone Foothills) andA03 (small non glaciated crystalline streams, biore-gion Non-Glaciated Central Alps) was channel altera-tion/channelization. The final index proposed for thesestream types comprises nine metrics out of seven met-ric types in stream type A02, and seven metrics outof five metric types for stream type A03. DE’s varybetween 50 and 100%. Some metrics with lower DE’sthat showed no linear dose-response to the targetedstressor (e.g., abundance of Oligochaeta) are also in-cluded because they clearly separate poor or bad frommoderate conditions.

The DE values of the indices are 91.7 (stream typeA02) and 83.3 (stream type A03). Nevertheless, thestatistical power is potentially high, indicated by Co-hen’s d = 2.04 (A02) and 2.10 (A03), respectively(Table 5).

The investigated streams of type A04 (mid sizedstreams in the Bohemian Massif) were stressed byimpoundment. From a total of 26 candidate metrics18 were removed because of redundancy. Finally 8metrics representing 6 different metric types remained.The DE’s of single metrics vary between 83 and 100%,but the index shows a DE of 100% and Cohen’s d

= 3.02, which means there is no overlap betweenreference/slightly disturbed sites and stressed sites.Discrimination between all pre-defined quality classeswas comparable (Fig. 5).

Definition of threshold values for ecological qualityclasses

Thresholds were derived from the distribution of in-dex scores in the pre-classified streams. For proposedclass boundaries in the AQEM indices variable classranges were defined because not all of the involvedmetrics show a linear relationship to stressor intensity.Suggested threshold values are summarized in Table 6.

To check the performance of the model, the finalindex classes were compared with the estimated con-dition of the pre-classification. The results are shownin Table 7. The rating of the investigated sites using thefinal index shows that a total of 36 samples had to bereclassified applying the multimetric index. Within thefinal classification 20 sites were rated to lower qualityclasses, 16 are rated to higher classes. None of thesites had to be reclassified more than one categoryaway from the pre-classification. The most sampleshad to be reclassified in stream type A02 (11) and A03(12). Within stream type A01 seven and within streamtype A04 six sites were re-classified. Concerning themost crucial discrimination between good and moder-ate, two samples were reclassified in stream type A02,and one sample in all other stream types.

Temporal and spatial variability of core metrics andindices under reference conditions

To compare temporal and spatial variability of met-rics, we calculated coefficients of variation of metricvalues between both sample seasons, and among allreference sites in each season, respectively (Table8). The coefficients of variation show strong distinc-tions in the temporal and spatial variability of dif-ferent attributes. Unsurprisingly, metrics comprisingabundance information (abundance measures, zona-tion/current preference measures) were the most vari-able while taxa richness measures, taxa composi-tion measures, tolerance/intolerance measures, func-

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Table 6. Suggested threshold values for the investigated streamtypes/stressors

Stream type

A01 A02 A03 A04

Ecological quality boundaries proposed threshold values

High-good 0.65 0.83 0.77 0.78

Good-moderate 0.47 0.67 0.69 0.68

Moderate-poor 0.35 0.51 0.6 0.51

Poor-bad 0.19 0.36 0.43 0.4

tional/trophic measures as well as the diversity index(Margalef) show clearly lower variations. Some ex-amples of metrics with diverse variability and rangeswithin the two investigated seasons but still highpower to discriminate between reference/slightly dis-turbed sites and stressed sites are shown in Fig. 6.Various types of response were found: Type 1: Hightemporal variability within reference conditions andunder stress (e.g. sensitive taxa, stream type A04),type 2: high variability between seasons within ref-erence sites, low variability in stressed sites (typicalfor abundance measures, e.g., number of Trichopteraindividuals, stream type A04), type 3: low variation inthe values of reference and high variation under stress(e.g., [%] littoral & profundal, stream type A01), type4: low variation between seasons, but relatively highvariation in the ranges under both stressed and undis-turbed conditions (e.g., # EP-taxa, stream type A01).

Discussion

Several different metric types were included into themultimetric indices and provided appropriate meas-ures to distinguish between different degrees of stress.Most of the selected metrics are based on either rel-ative abundance within specific or functional groupsor number of taxa within taxonomic groups. Richnessmeasures reflect the diversity of an aquatic assemblageand are known to be most useful in indicating impair-ment (Resh et al. 1995). Elimination of taxa from anaturally diverse system can be easily detected (Bar-bour et al., 1996). In particular, species belongingto the insect orders Ephemeroptera, Plecoptera andTrichoptera (EPT) are generally regarded as sensitiveto impairment and the loss of taxa richness within thisgroup indicates perturbation (Wallace et al., 1996).

Richness measures (number of total taxa, number oftotal families, EPT-Taxa, EP-Taxa) contribute to allof the indices developed showing consistently highdiscriminatory power.

Abundance measures are known to have high vari-ances and are rarely used in multimetric approaches(Barbour et al., 1999). However, abundance measuresare explicitly demanded in the WFD. Relative ratherthan absolute abundance is preferably used becausethe relative contribution of individuals to the totalfauna reflects interactive principles and is more in-formative than abundance data on populations withouta knowledge of the interaction among taxa (Plafkinet al., 1989; Barbour et al., 1999). Nevertheless, fourdifferent abundance measures were selected for theindices because in some cases they are useful, espe-cially if the disturbance is mainly related to physicalchanges within a river section. As an example, thenumber of Plecoptera taxa may be more or less con-stant in natural as well as in straightened sections,but the number of individuals decreases dramatic-ally due to declining amount of adequate habitatsand clearly distinguishes between different degrees ofstress. At least one of the absolute abundance meas-ures of Plecoptera, Trichoptera or Oligochaeta taxa arecomponents of each index. Although they partly showhigh spatial and temporal variations they were gooddiscriminators between low and high effected sites.Logarithmic transformation of abundance measuresis better than abundance in almost every application,and we recommend using log abundance rather thanabundance.

Composition measures provide information on therelative contribution of taxonomic groups to the totalfauna. The premise is that a healthy and stable as-semblage will be relatively consistent in its propor-tional representation (Barbour et al., 1996). Threedifferent composition measures contribute to the in-dices: [%] EPT taxa, showing high discriminatorypower in stream type A02 and [%] EP taxa in streamtype A01 whereas [%] Oligochaeta & Diptera taxawere most powerful in stream types A03 and A04.

Tolerance/Intolerance measures include pollutiontolerant and intolerant taxa or percent composition andare generally not specific to stressor types (Barbouret al., 1999). This was confirmed by our results, wherethe percentage of Austrian Sensitive Taxa (Moog et al.,1999) was responsive in 3 of 4 stream types. It was notused in stream type A04 (Bohemian Massif).

Functional/Trophic measures comprise informa-tion on production, trophic dynamics and interaction

265

Figure 6. Examples of metrics with different variability and different ranges within investigated seasons (ref-spring: reference sites in spring,ref-summer reference sites in summer, stress-spring: stressed sites in spring, stress-summer: stressed sites in summer).

and availabilty of food sources (Cummins & Klug,1979; Karr et al., 1986; Plafkin et al., 1989). Measuresof functional feeding guilds are percentages of singlefeeding types within the entire assemblage. Special-ised feeders such as scrapers or shredders are regardedas more sensitive, while generalists as collectors or fil-ter feeders are more tolerant to impairment, able to usea wider range of available food materials (Cummins& Klug, 1979; Barbour et al., 1996). The percent-age of gatherers and collectors was found to be astable measure for streams stressed by impoundment(stream type A04) increasing clearly with increasingstress. Percent shredders showed reliable response toorganic pollution (stream type A01) declining withincreasing pollution. The Rhithron Feeding IndexRETI (Schweder, 1992), a composite of [%] shred-ders & grazers, was included in the index for streamtype A03 stressed by channel alteration and straight-ening. As mentioned above, this is an example of ameasure which shows different reactions depending onthe kind of stress. Straightening enables an increasingproportion of this feeding type, but other stressors, e.g.organic pollution, decline with channel alteration.

The Longitudinal Zonation Index (Moog et al.,2001b) was integrated in the index for stream type

A04, distinguishing clearly between all pre-classifiedecological quality classes. Percent littoral & pro-fundal preferences and [%] littoral preferences arecomprised in the indices for stream type A01 (Hun-garian Plains) and A03 (Non-Glaciated Central Alps)respectively. In case of stream type A03 the power of[%] littoral & profundal preferences to separate ref-erence sites from stressed sites is low, but it clearlydiscriminates high affected sites from all other sites.

Margalef’s diversity index (Margalef, 1958) gavereliable results in all stream types. This measure con-tributes to the indices of all stream types except streamtype A04.

The resulting indices comprise seven to nine fromfive to seven metric categories and distinguish refer-ence/slightly disturbed sites from stressed sites withprobabilities between 99.8% and 100%. Differentlevels of the stressors were detected with high ef-ficiency in all stream types. The multimetric indexgives significantly better and more consistent resultsthan any of the individual metrics, showing higherdiscrimination efficiency and lower spatial and tem-poral variation. Best results were obtained under or-ganic pollution and impoundment, where the indexclasses showed nearly no overlap between the original

266

Table 7. Number of sites reclassified to different quality classes applying thresholds of the multimetric indexin comparison to the pre-classification. Reclassification error types: ‘Lower’. . . classification to lower class,‘Higher’. . . classification to higher class

Reason for A01 (24) A02 (26) A03 (26) A04 (24) Total (100)

reclassification Total Good/ Total Good/ Total Good/ Total good/ Total Good/

mod. mod. mod. mod. mod.

Lower 6 1 4 0 7 0 3 1 20 2

Higher 1 0 7 2 5 1 3 0 16 3

Total 7 1 11 2 12 1 6 1 36 5

classes using abiotic criteria. One reason might be thatfor these disturbance types the degree of stress waseasier to verify with abiotic measures. Channel altera-tion/channelization is the most difficult to pre-classify.For the stressor channel alteration, several metrics re-sponded in the opposite direction from the expectedresponse. In particular, the change in habitat compos-ition caused by straightening can simulate more up-stream conditions and can lead to a reverse response ofmetrics such as distribution of biocoenotic preferencesor the share of shredders and grazers (RETI). Com-position measures (e.g., [%] EPT taxa) are sometimesnot appropriate for channelization, because changesmay favour organism groups living on stony bottomsubstrate (e.g., scrapers or clingers) while soft bottomsubstrates may be missing or strongly reduced.

The efficiency of metrics and indices was testedagainst a pre-classification, which may be question-able in some cases. A pre-classification for testingmetrics is necessary to get an idea of stressor response,but it is exclusively based on abiotic parameters. Ac-cording to the EU Water Framework Directive theassessment of the ecological state of a water bodyhas to be based on the values of the biological qual-ity elements, namely the changes in the compositionand abundance of invertebrate taxa from the type-specific communities implying that the biota and notthe abiotic conditions are regarded as the fundamentalassessment criterion.

Although the number of sites investigated inAQEM was comparatively small, we were able todetermine spatial and temporal variability. A usefulindex should be consistent over time at a site if nochange in quality occurs during the sample periodand should show only slight variation among sites ofsimilar quality (Karr et al., 1987). A low variabilityof measures within reference conditions is deman-ded by the WFD and the EC REFCOND Working

Group (2001), but high spatial and temporal variab-ility of metrics within reference condition does notnecessarily mean that a measure is useless. Althoughsome metrics showed significant differences in spatialand/or temporal distribution under reference condi-tions, they are still appropriate measures to distinguishbetween reference sites and stressed sites.

In summary, the multimetric approach as testedduring the AQEM project was found to be useful andsensitive to evaluate the ecological status of runningwaters in Austria. It was able to detect changes in thebiota along a gradient of different stressors within dif-ferent stream types. The indices developed here werehighly specific, for a unique combination of bioregionand stressor type; furthermore, the sample size in eachbioregion was necessarily small due to the constraintsof the AQEM project. Therefore, the preliminary in-dices are unlikely to be effective for assessing allstressor types in these bioregions. Development of in-dices for full implementation in Austria will requireeither generalization to multiple stressor types, or fur-ther development of stressor-specific indices (as wasdone here) for those stressors not examined in thepresent study. Generalization can proceed by combin-ing metrics shown effective for individual stressors inthis study; development of additional stressor-plus-bioregion specific indices would require data fromstressor gradients not investigated here (e.g., organicenrichment in the Alps bioregion). For a nationwideapplication of the multimetric system it is not neces-sary to calculate individual indices for each streamtype. Indices should be separately developed only forlarge faunal differences (bioregions). The idea is tocreate an overall index for each bioregion using a setof basic metrics, and to provide derived indices in caseof assessing distinct single stressors by adding or ex-cluding specific metrics. The final threshold values arecalibrated for every stream type.

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Table 8. Temporal variation (coefficient of variance [CV, %] within different seasons) and spatial variation (CV [%] within allsites in each single season) of core metrics and the final index in the population of reference sites

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