Hydrobiologia 516: 269–283, 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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The effect of taxonomic resolution on the assessment of ecological waterquality classes

Astrid Schmidt-Kloiber1 & Rebi C. Nijboer2

1BOKU, University of Natural Resources and Applied Life Sciences, Vienna, Max Emanuel Straße 17,A-1180 Vienna, AustriaTel: +43-(0)1-47654/5225. Fax: +43-(0)1-47654/5217. E-mail: astrid.schmidt-kloiber@boku.ac.at2Alterra, Green World Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands

Key words: taxonomic resolution, ecological quality class, benthic invertebrates, bioassessment, Water FrameworkDirective, European stream types

Abstract

Within the ecological assessment of running waters based on benthic macroinvertebrates different levels of taxo-nomic resolution (species, genus, family and higher) are in use. Although assessment systems are often developedwith detailed data on species level, water managers and other end-users could like to use data on higher taxonomiclevels to assess the ecological quality of a water body because of limited human or money resources. The questionthat arises is, if an assessment system built with species level data is also applicable using data with a highertaxonomic resolution.

Within the AQEM project a multimetric assessment system was developed to evaluate the ecological qualityclasses (from bad (1) to high (5) ecological quality) of different stream types throughout Europe. The present studyfocuses on the question whether the resulting water quality class changes using the AQEM Assessment Software(AAS) with different taxonomic resolutions and if yes, how large the deviations of ecological quality classes fromthe original classes are and if the deviations are unidirectional. For analyses data from four Austrian and two Dutchstream types were used.

It is demonstrated that the assignment of a site to an ecological quality class may change if different taxonomiclevels are used. Deviations in both directions (higher/lower ecological quality class) were observed. In most casesthe divergence was only one ecological quality class, but also larger deviations occasionally occurred. The causes ofchanges in the assessment were investigated by separately looking into the underlying metrics of the multimetricsystem. Some of the evaluated metrics rely on autecological information on species level and are simply notapplicable on higher taxonomic levels. Other metrics worked on higher taxonomic levels as well and showed moreor less good distinctions between ecological quality classes.

It is concluded that the AQEM Assessment Software is not applicable if data on higher taxonomic levels areused. As the deviations were not unidirectional and ranged from one to three ecological quality classes, it is notpossible to include a correction factor for using the software with higher taxonomic resolution data.

Introduction

Benthic invertebrates often play a major role in theecological evaluation of water bodies (Rosenberg &Resh, 1993). For the ecological assessment of run-ning waters different levels of taxonomic resolution(orders, families, genera, species) are currently inuse (for an overview see Resh & McElravy, 1993).

Theoretically, if species are combined to a higher taxo-nomic level, an ecological statement is only possibleif the requirements of the different species within thistaxonomic level are known and if these are similar.However, systematics of macroinvertebrates and thedefinition of species are often based on morphologicalfeatures (in most cases reproductive organs) and not on

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functional characteristics. Therefore, species withina genus may have different ecological requirementsand the response of species within a genus to differ-ent impact factors or stressors may vary widely (e.g.,Moog, 1995). Ecological information provided by us-ing higher taxonomic units, like genera or families, inenvironmental analyses may therefore bias the resultsand may reflect only a poorly defined image of thesituation.

Benefits of species level information in ecologicalstudies are mentioned by several authors (e.g., Resh& Unzicker, 1975; Resh & McElravy, 1993; Mooget al., 1997; Stubauer & Moog, 2000, Verdonschot,2000; Lenat & Resh, 2001) and therefore, assessmentsystems are frequently built using detailed data on thelowest taxonomic level possible. However, water man-agers and other end-users of assessment systems oftenuse data on higher taxonomic level because of:

• cost efficiency: small financial budgets often onlyallow identification to genus or family level;

• lack of time or human resources: if very largenumbers of sites must be sampled every yearwith restrictions on the number of personnel hightaxonomic level identification is the only feasiblemethod;

• lack of taxonomic expertise: because of an inappro-priate sampling date identifiable stages of macroin-vertebrates may be absent or a high number ofjuvenile specimens make species level identifica-tion error-prone or even impossible. Moreover, thelack of identification keys is a serious problem forsome taxonomic groups, sometimes there are notaxonomic descriptions available at all;

• lack or unavailability of autecological information:in many groups of aquatic invertebrates the literat-ure is scattered and only known to specialists. If theecological requirements of species are not knowntheir use for ecological assessment is minimisedand the motivation of identifying them decreases.

Some authors have developed assessment systemsbased on genera or families, e.g., the BMWP/ASPT-method (Armitage et al., 1983) or the RIVPACSsystem (e.g., Wright, 2000). Genus or even familylevel may be sufficient to detect differences for a rapidassessment of water quality or a demonstration ofbiotic relationships on a broader scale (geographic-ally seen). Probably large differences in environmentalconditions can be identified by shifts in numbers ofindividuals and taxa of genera or even families.

As the Water Framework Directive (WFD;European Commission, 2000) demands an accurateclassification and high discriminative power in assess-ing European rivers, a European assessment systembased on species level was aimed for within the pro-ject ‘The Development and Testing of an IntegratedAssessment System For the Ecological Quality ofStreams and Rivers Throughout Europe using BenthicMacroinvertebrates’ (AQEM; Hering et al., 2004). Thedeveloped evaluation system was finally implemen-ted in the AQEM Assessment Software (AAS; Heringet al., 2004). The base of the AAS are multimetricindices for a number of combinations of stream typesand stressors (e.g., see Brabec et al., 2004; Buffagniet al. 2004; Sandin et al., 2004), each consisting of acombination of metrics that can – aggregated together– discriminate between five ecological quality classes(from bad (1) to high (5) ecological status). In futurethe system will be used by water authorities and man-agers as well as other applied users. Although mainly(near to) species level data were used for the devel-opment of the multimetric indices, future users couldtry to apply the AAS on higher taxonomic levels con-cerning one of the above listed reasons. The presentstudy therefore focuses on the question whether theAAS which was built on a fine scale of taxonomic res-olution performs similarly if data on higher taxonomiclevels are used. The second question that is addressedis, whether the resulting ecological quality classes de-viate from the ecological quality classes achieved withspecies level data, as well as whether these deviationsare acceptable (e.g., is the deviation only one ecolo-gical quality class or more?) and always in the samedirection (e.g., will the use of higher taxonomic levelsalways result in higher ecological quality classes?). Ifthe deviation is always in the same direction a correc-tion factor could be added to the system to extend itsapplication to higher taxonomic levels.

Material and methods

Sampling sites, data collection and processing

The European Water Framework Directive (WFD)stipulates that member states have to assess the eco-logical status of a water body by comparing thepresent status to the expected reference condition.The AQEM project used a typology-based approachfor the development of an ecological assessment sys-tem for European streams. Consequently, a number

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of factors were used to partition the natural variabil-ity expected to occur at a stream site; streams wereclassified by ecoregion, altitude, and catchment size.Human-generated disturbance gradients (e.g., organicpollution, morphological degradation) of sites wereselected and sampled for each stream type. A min-imum of 11 sites was chosen for each stream type, 3sites of high ecological status (reference conditions,class 5), 3 sites of good ecological status (class 4), 3sites of moderate ecological status (class 3) and 1 siteeach of poor (class 2) and bad ecological status (class1). Definitions of ecological quality classes followed apre-classifiaction system based on abiotic factors andexpert consensus (Hering et al., 2004).

At each site macroinvertebrates were collected fol-lowing the AQEM protocol for sampling and sortingstrategy (Hering et al., 2003). Whenever possible theindividuals were identified to species level (limita-tions were given because of first instars or lack oftaxonomic knowledge of some taxa). For our studywe used the data of four Austrian and two Dutchstream types (Table 1). For a detailed description ofthe stream types see Ofenböck et al. (2004) and Vleket al. (2004).

Taxonomic adjustment

Basically the terms ‘high’ and ‘low’ taxonomic resol-ution are used in two different ways. On the one handa ‘high’ resolution indicates species level, on the otherhand a ‘high’ taxonomic level is understood as genus,family, order or higher systematic unit. In this paperwe follow the second approach and the lowest possiblelevel is ideally defined as species level. The compar-ison of different taxonomic resolutions was carried outusing three data sets for each stream type:

• original data (mainly species level): the originaltaxa-lists of the sites were used without any adjust-ment;

• genus level: the abundances of all species withina genus were summed up; this procedure was alsodone, even if there was only one species within thegenus; higher levels (families) were only kept incase there was no genus determined;

• family level: the abundances of all species andgenera within one family were summed up; thisprocedure was also done, even if there was onlyone genus/species within the family; higher levels(orders, classes) were only kept in case there wasno family determined.

Metrics, multimetric indices and assessment of theecological quality class

The individual metrics, the multimetric indices and thefinal assessment of the ecological quality classes werecalculated with the AAS using the data of the threetaxonomic levels.

For the Austrian stream types the calculationmethod implemented into the software is based onone multimetric index per stream type and investig-ated stressor. For each index more than 200 biologicalmetrics were tested for their sensitivity to the targetedstressors and evaluated against a pre-classification ofsites into five ecological quality classes. The pre-classification of the sites followed physical, chemical,and land use criteria as well as expert consensus(Ofenböck et al., 2004). The metrics were also ex-amined for their spatial and temporal variability andtheir efficiency to discriminate between different de-grees of stress. After the removal of redundant metricsthe remaining ones were transformed to a score andfinally combined to a multimetric index by averagingthe scores. Aggregating the metrics into indices em-phasis was given to cover a number of different metrictypes according to the AQEM classification follow-ing the ‘kingdom of metrics’ (Karr & Chu, 1999).Choosing this approach different dimensions of theecosystem are integrated and the community healthis evaluated using, e.g., information on communitystructure, population balance or several functionalgroups (Karr, 1991; Barbour et al., 1995; Gerritsen,1995). Combinations of metrics were selected to dis-tinguish best between non or slightly impaired andstressed sites (evaluated by calculating discriminationefficiency values and power analysis). In the end fixedthreshold values were set to define the five ecologicalquality classes and sites were re-classified accordingto these newly defined boundaries (final classifica-tion). For a detailed description of the index develop-ment, selection of metrics, test on spatial and temporalvariability and statistical power see Ofenböck et al.(2002, 2004).

As an example, the metrics, that form the in-tegrated index for assessing stream degradation (im-poundment) of rivers in the Austrian Granite & GneissRegion of the Bohemian Massif (stream type A04)will be further discussed in this paper. After the pro-cess of metric selection and exclusion of redundantmetrics, finally eight metrics representing six differ-ent metric types remained for the A04-index. Thediscrimination efficiencies of single metrics to dis-

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Table 1. Investigated stream types, including bioregion, ecoregion and investigated degradation factors (Ofenböck et al., 2004; Vlek et al.,2004)

Country Stream type/bioregion No. Ecoregion No. of Investigated

sites stressor

Austria Mid-sized streams in theHungarian Lowlands/EasternRidges and Lowlands

A01 Hungarian Lowlands (11) 24 Organic pollution

Austria Mid-sized calcareouspre-alpine streams/LimestoneFoothills

A02 Alps (4) 26 Stream morphology(channelisation)

Austria Small non-glaciated crystallinealpine streams/Non-glaciatedCrystalline Alps

A03 Alps (4) 26 Stream morphology(channelisation)

Austria Mid-sized streams in the Bo-hemian Massif/ Granite &Gneiss Region of the BohemianMassif

A04 Central Highlands (9) 24 Stream morphology(impoundment)

The Netherlands Small Dutch lowland streams N01 Western Plains (13), Central Plains (14) 142 General degradation

The Netherlands Small Dutch hill streams N02 Central Plains 14 14 General degradation

tinguish between reference and stressed sites variedbetween 83% and 100%, the index finally reached100% regarding the pre-classification of samplingsites. That means there was no overlap betweenreference/slightly disturbed sites and stressed sites.Also the discrimination between all pre-defined qual-ity classes reached comparable precisions (Ofenböcket al., 2003). The following metrics were included inthe index: total abundance, number of taxa, numberof Ephemeroptera, Plecoptera, and Trichoptera (EPT)-taxa, percent Oligochaeta & Diptera taxa, abundanceof Trichoptera, longitudinal zonation index, percentpreference for littoral and percent gatherer/collectors.

The Dutch multimetric assessment system uses tenmetrics (Vlek et al., 2004). Each one of these tenmetrics is able to differentiate between one particu-lar ecological quality class and the other three qualityclasses (reference sites, class 5, were not included).If the individual metric scores between the 25th and75th percentile of its variation for an ecological qualityclass the sample is assigned to the respective ecolo-gical quality class. There is no result for the metric ifthe score is below the 25th or above the 75th percent-ile. The final ecological quality class is calculated byaveraging the individual metric results (multimetric).If no results for any of the ten metrics calculated fallwithin the 25th/75th percentile range no classificationsare made and the final result for the ecological qualityclassification is ‘unknown’ (Vlek et al., 2004).

Evaluation of metric types

To explain the reason why a site – applying the AAS– might be placed into a different ecological qualityclass from the original class achieved with the specieslevel data, the metrics used within the multimetric in-dex for stream type A04 were calculated with differenttaxonomic levels. In a first step different metric types(e.g., richness, composition, diversity measures) wereevaluated whether they theoretically work on highertaxonomic levels or not. Necessary ecological inform-ation was taken from the current AQEM/STAR taxadatabase (www.aqem.de; www.eu-star.at), in whichdifferent autecological features – such as saprobic ormicrohabitat preferences – are stored as numericalvalues.

In a second step, for the metrics that should theor-etically work if higher taxonomic level data are used,the discrimination efficiency was calculated applyingthe different taxonomic levels. The clear separationbetween high and good ecological quality on the onehand and moderate, poor and bad ecological qualityon the other hand is one of the major goals whenimplementing the WFD, because if a water body isclassified as moderate or worse, action to improve thequality of the respective river section is required. Asto these reasons all reference and good sites as wellas all moderate, poor and bad sites were pooled forthe calculation of the discrimination efficiency accord-ing to their final classification. This approach led to

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Table 2. Number of taxa included in the data sets for differenttaxonomic levels

Adjustment type A01 A02 A03 A04 N01 N02

Original data 391 391 298 383 865 318

Genus level 182 144 123 169 355 163

Family level 64 59 46 62 111 69

an almost equal number of sites within each of thesetwo groups. The discrimination efficiency was calcu-lated as the percentage of stressed samples (moderate,poor, bad quality) with metric values lower than the25th-percentile of reference values (high, good qual-ity) for decreasing metrics and the 75th-percentile forincreasing metrics respectively (Major et al., 2001).

Visual assessment of metrics regarding differentecological quality classes was done applying Box andWhisker Plots. For this purpose the software packageSTATISTICA 5.5 (StatSoft Inc., 2000) was used.

Results

Applying the AQEM Assessment Software (AAS)

The adjustment for different taxonomic resolutionsresulted in different numbers of taxa used for analyses(Table 2). Aggregating the original data to genus levelthe number of taxa was reduced to at least 50%. Usingfamily level data the number of taxa decreased to aboutone fifth of the original number.

The ecological quality classes achieved with theAAS using the original (near to) species level dataset (hereafter referred to as ‘original classes’) werecompared to the results of the two other taxonomicresolutions. The deviations from the original classesfor all stream types are shown in Table 3.

At genus level 57% of 100 investigated sites inAustria and 45.5% of 156 sites in the Netherlands wereclassified into different ecological quality classes thanexpected by the use of species level data. At fam-ily level 60% of the Austrian and 27% of the Dutchstreams were classified differently. In three streamtypes genus level showed fewer differences comparedto the original data results than family level and in theother three stream types it was vice versa.

In Austria most sites were classified into lowerecological quality classes if higher taxonomic resolu-tions were used. In only eight cases ecological quality

classes higher than the original classes were calcu-lated. This trend did not entirely apply to the Dutchstream types: on genus level results were mainly oneclass higher and on family level results were mainlyone class lower. Whereas in the Dutch data no devi-ations of three classes occurred, especially the resultsfrom the two alpine stream types (A02, A03) showedclassifications of three classes lower.

General comments on the performance of metrics onhigher taxonomic levels

An estimation of the performance of metrics onhigher taxonomic levels shows that metrics compris-ing abundances or total numbers and percentages ofthese values can always be calculated on higher taxo-nomic levels (Table 4). Regarding metric types thisapplies for richness, composition, and diversity meas-ures. The use of all other metrics, which are basedon any kind of ‘ecological classification’ like cur-rent preference measures or habitat/mode of existencemeasures, may be restricted in terms of ecologicalinformation. As to different ecological requirementsof species belonging to one genus or family thoseclassifications are often not available on higher taxo-nomic levels. Merely functional and trophic measuresperform on higher resolutions.

Index-example for stream type A04 (mid-sizedstreams in Granite and Gneiss Region of theBohemian Massif)

The Box and Whisker Plots of the multimetric indexin the Austrian Granite & Gneiss Region show clearseparations of all ecological quality classes (Fig. 1).

Regarding the metrics calculation two out of eightmetrics (longitudinal zonation index, % preference forlittoral) of this multimetric index could not be calcu-lated on higher taxonomic levels, because those met-rics needed ecological information that was not avail-able on taxonomic levels higher than species level. Incontrast to this, for the metric % gatherer/collectorsseveral ecological classifications existed on genus andfamily level. The value and the reliability of the metrictherefore depended on the number of such classifica-tions. Figure 2 illustrates an example for a metric thatdoes not work (% preference for littoral) whereas Fig-ures 3 and 4 show applicable metrics (number of EPTtaxa, % gatherer/collectors) that are able to distinguishbetween the five ecological quality classes, each of thegraphs for all three types of taxonomic resolutions.

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Table 3. Classification results (in number of sites) using different taxonomic levels for all investigated stream types

A01 A04 A02 A03 N01 N02

Genus Family Genus Family Genus Family Genus Family Genus Family Genus Family

Same class 13 15 13 14 8 4 9 7 52 84 9 6

1 class lower 6 8 9 10 16 6 9 7 1 19 4

2 classes lower 2 14 7 5

3 classes lower 2 1 7

1 class higher 5 1 2 51 13 3 3

2 classes higher 14 2 2 1

3 classes higher

Total deviations 11 9 11 10 18 22 17 19 66 34 5 8

For those metrics that show distinctions betweenecological quality classes on higher taxonomic levels(as shown in Figs 3 and 4) the discrimination effi-ciency was calculated. Figure 5 and Fig. 6 illustratethe same two metrics as in Figures 3 and 4 (numberof EPT-taxa, % gatherer/collectors) but in this case therange of scores was compared between reference/goodsites (ecological quality classes 5 and 4) and stressedsites (ecological quality classes 3, 2, and 1) regardingdifferent taxonomic resolutions.

Table 5 shows the discrimination efficiencybetween the two groups of sites for the metrics of theA04-index that worked on a higher taxonomic level.To get an impression of the discrimination efficiencyfor metrics that were not included in the index theTable is extended with some metrics representing dif-ferent metric types. Abundance measures were not in-cluded in the Table because their values do not changeusing higher taxonomic levels. For two metrics – onebelonging to composition, the other to trophic meas-ures – included in the multimetric index, the specieslevel showed the highest (or equal to any other resolu-tion) discrimination efficiency. Regarding the richnessmeasures higher taxonomic resolutions showed higherdiscrimination efficiency. Basically, species level dataobtained the highest discrimination in the case of sixmetrics, genus level data in the case of three metricsand family level data in the case of seven metrics.

Discussion

The present study focused on the question whetherthe AAS – developed for the use of (near to) spe-cies level data – also can be applied using highertaxonomic resolutions as this might save human and

money resources. The evaluation of ecological qual-ity classes using taxonomic levels higher than specieslevel showed that about 50% of the sites were clas-sified differently on genus and 40% on family level.Whereas in Austria most sites were classified intolower ecological quality classes if higher taxonomiclevels were used, in the Netherlands the results indic-ated higher ecological quality classes for genus leveland lower classes for family level data.

Generally, ‘wrong’ results of the ecological qualityclass could on the one hand lead to unnecessary costs –if the assessed ecological quality class is lower than inreality – to improve the ecological quality of a streamwhile in fact it is not necessary. On the other hand amisclassification could result in environmental dam-age – if the assessed ecological quality class is higherthan in reality – because no restoration measures aretaken although they would be necessary.

For two stream types (A02, A03) using highertaxonomic levels always resulted in lower ecologicalquality classes, but the deviations from original classesranged between one and three classes. Regarding theother two Austrian stream types the divergences ofonly one class were sometimes higher, sometimeslower. For the Dutch stream types also lower andhigher classifications were observed and the differ-ences ranged from one to two classes. Even thoughthe results were different in individual stream types,the deviations were generally not always in the samedirection and also the number of varying classes didnot follow any scheme. Therefore, it is not possibleto add a kind of correction-factor to the AAS to over-come the problem of misclassification and to allow theuse of higher taxonomic resolutions. Hence, the ap-plication of data on higher taxonomic levels cannot berecommended when using the AAS, because type and

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Table 4. Estimation of the performance of metrics on higher taxonomic levels

Metric type Example Applicable on a higher taxonomic level

Richness measures Total number of taxanumber of EPTa-taxa

Yes

Composition measures % dominant taxon% Trichoptera

Yes

Diversity measures Shannon & Wiener Indexb

Simpson diversity indexcYes

Similarity/loss measures Species deficitmissing taxa

Restricted; possible but mostly based on species levelinformation

Tolerance/intolerance measures Saprobic indexBMWPd, ASPTe

Partly; depends on the type of metric

Functional/trophic measures(feeding measures)

% grazerRETIf

May be restricted in terms of ecological classification

Habitat/mode of existencemeasures

% of clingersnumber of (semi)sessil taxa

May be restricted in terms of ecological classification

Current preference measures % limnophil% rheophil

No; mostly based on species level information

Longitudinal zonation measures % hypopotamalzonation index

No; mostly based on species level information

Generation turnover measures % bivoltin% univoltin

No information available at present

Individual condition measures Contaminant levels% diseased individuals

No information available at present

aEphemeroptera, Plecoptera, and Trichoptera.bShannon & Weaver, 1949.cSimpson, 1949.dBiological Monitoring Working Party score/family (Armitage et al., 1983).eAverage Score per Taxon = BMWP score divided by number of BMWP families (Armitage et al., 1983).fRhithron Feeding Type Index (Schweder, 1992).

dimensions of the deviations of quality classes cannotbe forecasted. Future users of the AAS are thereforestrictly advised to use the taxonomic level for whichthe software was developed.

These results indicate that using higher taxonomicresolutions with the AAS lead to wrong estimations ofecological quality classes. This is clearly caused bythe fact that the underlying multimetric indices andthe definition of class boundaries which finally res-ult in the evaluation of ecological quality classes, aredesigned and tuned for species level data.

Considering for example the multimetric index ofone Austrian stream type (A04; mid-sized streams inthe Granite and Gneiss Region of the Bohemian Mas-sif) two metrics (% preference for littoral, longitudinalzonation index) could not be calculated because theyare based on numerical expressed ecological prefer-ences of the taxa that are often not available on highertaxonomic levels, either because they are not knownor because it is not possible to designate the samenumerical values to different species within a genusor family (e.g., Fig. 2). Table 6 shows the number of

ecological classifications for different taxonomic res-olutions regarding three metrics: saprobic preferences,stream zonation preferences (which form the base forthe two above mentioned metrics) and feeding types.This information is based on the AQEM/STAR taxadatabase, currently comprising 9557 European taxa.The relevant point is the proportion of taxa desig-nated to those without classifications. For the saprobicpreferences and the stream zonation preferences mostecological information is available on species level(23% respectively 28%), decreases on genus level(13% respectively 4%) and finally is lowest on familylevel (2%). The number of ecological classificationsobviously seems to be very low, but it depends onthe taxonomic group considered. Regarding only EPT-taxa for example, more designations are available thanfor most Diptera. In contrast to this the numerical clas-sifications of functional feeding types show that 32%of the species, 43% of the genera and even 56% of thefamilies are designated concerning feeding habits. Tosummarise, the reliability of a metric representing anecologically based metric type depends on the avail-

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Figure 1. Multimetric index for streams in the Austrian Granite & Gneiss Region of the Bohemian Massif; 5 indicates ecological quality class‘high’, 1 indicates ‘bad’.

Figure 2. Metric ‘% littoral’ for different taxonomic resolutions over all water quality classes.

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Figure 3. Metric ‘number EPT-taxa’ for different taxonomic resolutions over all water quality classes.

Figure 4. Metric ‘% gatherer/collectors’ for different taxonomic resolutions over all water quality classes.

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Table 5. Discrimination efficiency for metrics working on higher taxonomic levelsindicating best (or equal) values in grey

Type Discrimination efficiency %

Metrics of the index-example (A04) Species Genus Family

No. of taxa richness 83.33 100.00 100.00

No. of EPT-taxa richness 91.67 91.67 100.00

% Oligochaeta & Diptera-taxa composition 91.67 91.67 41.67

% gatherer/collectors trophic 100 91.67 91.67

Examples for other metrics

EPT-taxa/Oligochaeta richness 58.33 58.33 41.67

Shannon & Wiener Index diversity 83.33 66.67 83.33

Margalef Indexa diversity 75.00 75.00 91.67

RETI trophic 91.67 83.33 91.67

% current preference type rheophil current pref. 83.33 66.67 91.67

% river zone hypocrenal longitudinal 91.67 83.33 91.67

aMargalef, 1958.

Table 6. Number of numerical expressed ecological classifications within the AQEM/STAR taxa database for saprobic prefer-ences, stream zonation preferences and feeding types (* indicates a higher number of taxa than for the other two metrics becauseof inclusion of adult Coleoptera)

Saprobic preferences Stream zonation preferences Feeding types

Species Genus Family Species Genus Family Species Genus Family

Total number 6360 1427 260 6360 1427 260 7391∗ 1534∗ 260

No. of classifications 1462 185 5 1786 57 6 2332 662 145

% 22.99 12.96 1.92 28.08 3.99 2.31 31.55 43.16 55.77

ability and the number of ecological classificationsregarding the analysed level.

All other metrics (total abundance, number of taxa,number of EPT-taxa, % Oligochaeta & Diptera taxa,abundance of Trichoptera, % gatherer/collectors) ofthe A04-index showed more or less good distinctionsbetween the different ecological quality classes onhigher taxonomic levels (for examples see Fig. 3 andFig. 4). The results of the discrimination efficiencycalculations (Table 5) indicated that species level iden-tification not necessarily achieved the best distinctionbetween these two groups of sites. The two richnessmeasures (number of taxa, EPT-taxa) for exampleshowed the best discrimination on higher levels thanspecies level. The number of EPT-families apparentlydecreases in a more distinct way with decreasing eco-logical quality classes than EPT-genera or species do(Fig. 3). Furthermore, the scattering within the mod-erate/poor/bad sites and the overlap between the two

groups of sites is higher within EPT-species and –genera than – families (Fig. 5). Also diversity meas-ures or current preference measures may show gooddistinctions on higher taxonomic levels (Table 5).

In conclusion, the discrimination efficiencyshowed that several metrics worked with higher taxo-nomic resolutions as well and could therefore on prin-ciple substitute the species-level-metrics. This impliesthat an assessment system could also be developed us-ing higher taxonomic level data. The question whichtaxonomic level should be used for biological as-sessment based on macroinvertebrates is a heavilydiscussed issue. Basically, the adequate taxonomicresolution always depends on the particular aims ofa study. Following Resh & McElravy (1993) specieslevel identification is to be preferred because the spe-cies is to be seen as the basic biological unit with thehighest information content and its use increases thesensitivity and detection of subtle changes in ecolo-

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Figure 5. Comparison of undisturbed versus stressed ecological quality classes for the metric ‘number EPT-taxa’.

Figure 6. Comparison of undisturbed versus stressed ecological quality classes for the metric ‘% gatherer/collectors’.

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gical quality assessment. As illustrated in the nicheconcept each species has evolved special abilities toexploit resources and to cope with the heterogeneityof its habitat. The occurrence of specific species as-semblages is therefore a result of the present environ-mental conditions. In case autecological requirementsof characteristic species associations are well estab-lished they provide useful evaluation criteria for thestructural and functional quality of freshwater eco-systems making them powerful bioindicators for theecological status of aquatic habitats.

Resh & McElravy (1993) presented an overviewshowing advantages and disadvantages of differnttaxonomic levels. For routine monitoring family levelanalysis is often recommended because of lack of timeand effort reasons. Following the conclusions of War-wick (1988, 1993) and Reece et al. (2001) the useof higher taxonomic levels might be an advantageto reduce noise created by environmental heterogen-eity (such as seasonal changes) and stochastic events,which may mask the effects of human activities. Anumber of studies hold the view that species level datatend to support the conclusions made based on fam-ily level, or that higher taxonomic levels are sensitiveenough to detect environmental impacts (Marchant,1990; Chessman, 1995; Graça et al., 1995; Marchantet al., 1995; Bournaud et al., 1996; Bowman & Bailey,1997; Hewlett, 2000; Bailey et al., 2001; Metzeling &Miller, 2001; Reece et al., 2001). Zamora-Muñoz &Alba Tercedor (1996), for example, found that familylevel identification was sufficient for monitoring waterquality in Spanish streams. In contrast to this, compar-isons of the BMWP/ASPT-method (Armitage et al.,1983), which works on higher taxonomic resolutions,with the Austrian saprobic system (Austrian StandardsM6232, 1995) pointed out that in the most crucialwater quality classes (‘good’ and ‘good to moder-ate’) broad scatters of the values were given (Stubauer& Moog, 1996; 2000; Koller-Kreimel et al., 1997).This phenomenon is caused by the different stresstolerances and therefore saprobic designations of theparticular species within one genus. An overview ofAustrian species of the caddisfly genus Hydropsycheand their saprobic requirements expressed as numer-ical classifications according to the Fauna AquaticaAustriaca (Graf et al., 1995) is shown in Table 7.The saprobic values of the individual species rangefrom 0.6 to 2.8. A reliable statement on the saprobicquality class using a taxonomic resolution higher thanthe species level is therefore not possible. Similar ex-amples for the blackfly genus Simulium and the water

beetle family Elmidae were given in Stubauer & Moog(2000) as well as for the Austrian mayflies in Mooget al. (1997).

In contrast to this, the assessment of the func-tional diversity does not necessarily require identific-ations to species level as related species may havesimilar biological functions (Dolédec et al., 2000).This trend is also obvious regarding the AQEM/STARtaxa database in which more families and genera thanspecies are classified concerning their functional feed-ing habits (see above and Table 6). The functionalapproach for biomonitoring purposes is also suppor-ted by the use of biological traits (see, for example,Statzner et al., 1994; Townsend & Hildrew, 1994;Dolédec et al., 2000; Usseglio-Polatera et al., 2000).However, it should be taken into consideration thatclassifications on higher levels are often comprom-ises because rarely all species within one genus/familyhave exactly the same ecological requirements andmay differ considerably. Sometimes even within onespecies the nutrition may change during its ontogen-esis depending on environmental factors like foodavailability or stage of the instar (e.g., Bohle, 1983;Graf et al., 1992). This may therefore cause un-certainty in ecological statements when using highertaxonomic resolution. In some cases it may be helpfulto look at the number of species within the genus andgenera within the family respectively. In regions withfew species per genus and genera per family respect-ively, species and genus/family assessments wouldbe expected to perform similarly (Hawkins & Norris,2000; Hawkins et al., 2000; Lenat & Resh, 2001).

For the ecological assessment as demanded by theWFD the assignment of sites to quality classes is a ma-jor point. Studies concentrating on this issue showedthat higher taxonomic levels increase the possibility of‘misevaluating’ sampling sites (Hawkins et al., 2000;Lenat & Resh 2001; King & Richardson, 2002). Forexample Lenat & Resh (2001) assigned less preci-sion to family related indices concerning water qualityassessment. A similar message provide Furse et al.(1984) who – though reporting only a slight to moder-ate loss of sensitivity when using family identifications– found better classifications of sites and predictionsof site groups from information on species than onhigher taxonomic levels. Also, some of the researcherswho state that a higher taxonomic resolution may beenough for water quality assessment, plead for speciesidentification if the exact biological responses to stressare required (Zamora-Muñoz & Alba-Tercedor, 1996).A similar conclusion is drawn by Waite et al. (2000)

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Table 7. Saprobic preferences of Austrian Hydropsyche species according to the Fauna Aquatica Austriaca (Graf et al.,1995)

Saprobic preferences

Xeno Oligo b-meso a-meso Poly Saprobic index

Hydropsyche tenuis (Navas, 1932) 4 6 – – – 0.6

Hydropsyche fulvipes (Curtis, 1834) 1 7 2 – – 1.1

Hydropsyche dinarica (Marinkovic, 1979) 1 7 2 – – 1.1

Hydropsyche instabilis (Curtis, 1834) 1 4 5 – – 1.4

Hydropsyche saxonica (McLachlan, 1884) 1 4 3 2 – 1.6

Hydropsyche siltalai (Döhler, 1963) – 2 6 2 – 2.0

Hydropsyche incognita (Pitsch, 1993) – 2 5 3 – 2.1

Hydropsyche pellucidula (Curtis, 1834) – 2 5 3 – 2.1

Hydropsyche bulgaromanorum (Malicky, 1977) – – 8 2 – 2.2

Hydropsyche angustipennis (Curtis, 1834) – 1 5 4 – 2.3

Hydropsyche bulbifera (McLachlan, 1878) – – 6 4 – 2.4

Hydropsyche modesta (Navas, 1925) – – 2 8 – 2.8

Hydropsyche contubernalis (McLachlan, 1865) – – 2 8 – 2.8

and Hawkins & Vinson (2000), who recommend – ifbudgets allow – a better taxonomic resolution becausegenus/species data reveal the same bioassessment andlandscape patterns, but with stronger statistical power.They also allow more detailed ecological interpreta-tion than family level data.

Moreover, regarding the demands of the WFDfor biological diversity and sensitivity, rare and en-dangered species will probably not be recognised us-ing higher taxonomic resolutions. Apart from natureconservation aspects, these relicts may have specificenvironmental requirements that have to be consideredin a special way and may help to evaluate the ecolo-gical quality of a running water (Resh & McElravy,1993). To facilitate the decision what taxonomic levelof the metrics should be integrated into the index itshould also be kept in mind that the design of a mul-timetric index implies the idea to cover the wholebenthic ecosystem to reliably assess the ecologicalquality class. For this purpose Karr & Chu (1999) –but also the WFD – explicitly insist on selecting coremetrics of all relevant metric types within the ‘king-dom of metrics’. Regarding the different metric typesthat should be included in the final index it is clearlyto be stated that many of them – especially those basedon any kind of ecological information, e.g., currentpreferences, longitudinal zonation or tolerance meas-ures - are not avail- or applicable on higher taxonomiclevels. Hence, information on parts of the functionaldimensions of the ecosystem is lost and one of the

fundamental ideas of multimetric bioassessment istherefore neglected.

Conclusion

From our study we conclude that the AQEM Assess-ment Software (AAS), built on mainly species leveldata, cannot be applied with data on higher taxonomiclevels. Deviations in ecological quality classes usingdata on genus or family level are pretty high from theresults obtained with the original data set. This couldlead to wrong conclusions about the ecological qual-ity of a water body. Underestimation of the ecologicalquality class may lead to an unnecessary increase incosts for restoration, overestimation of the ecologicalquality class may result in ecological degradation ofa water body. As the deviations sometimes resultedin higher and sometimes in lower ecological qualityclasses the assessment system cannot easily be adap-ted for the application with higher taxonomic leveldata. Users of the AAS are therefore strictly advisedto use the taxonomic level for which the software wasdeveloped.

Deviations of ecological quality classes were ex-pected because several of the core metrics analysedin this study are based on autecological informationthat is simply not available on higher taxonomic levels.Other metrics were applicable on genus or familylevel and also showed good discriminations betweenecological quality classes. However, for the final in-

282

dex development the basic idea of the multimetricapproach to address multiple functional dimensionsof biological systems should be kept in mind. Thisidea may be confined, if less metrics can be includedin the index because some metrics do not work onhigher taxonomic levels. The integrated multimetricapproach is highly dependent on the availability ofautecological information on species level. We there-fore share the view of Usseglio-Polatera et al. (2000)that continuing studies on fundamental life historytraits and the ecological requirements of species toimprove our knowledge on such biological and ecolo-gical information is one of our most important futuretasks.

Acknowledgements

We especially want to thank Wolfram Graf andThomas Ofenböck for their valuable and very helpfulcontributions to this manuscript, further Ilse Stubauer,Otto Moog, and Henk Siepel for their comments aswell as the editors of this issue and the two anonymousreviewers for the discussion and improvement of thispaper. Thanks to Daniel Hering for being ‘co-ordinatorof the century’ and to Doris Wohlschlägl-Aschbergerfor linguistical help. Funding of the AQEM projectwas provided by the European Union (Contract No.EVK1-CT-1999-00027).

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