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Page 1: Towards diagnostic biotic indices for river macroinvertebrates

Hydrobiologia 364: 169–182, 1998. 169c 1998Kluwer Academic Publishers. Printed in Belgium.

Towards diagnostic biotic indices for river macroinvertebrates

Bruce C. Chessman1 & Paul K. McEvoy2

Australian Water Technologies, P.O. Box 73, West Ryde N.S.W. 2114, Australia1 Current address: Department of Land and Water Conservation, P.O. Box 3720, Parramatta N.S.W. 2124,Australia2 Current address: Australian Water Quality Centre, P.M.B. 3, Salisbury S.A. 5108, Australia

Received 15 January 1997; in revised form 5 November 1997; accepted 18 November 1997

Key words:benthos, biological monitoring, biotic index, macroinvertebrates, water quality

Abstract

The construction of biotic indices that use macroinvertebrates to assess pollution and other anthropogenic distur-bances of rivers and streams often requires that each taxon be assigned a number indicating its level of sensitivity.A problem in constructing such indices is that individual taxa may vary quite widely in sensivity, depending onthe nature of the particular disturbance. One possible means of overcoming this problem is to construct a suite ofindices, each assembled using sensitivity numbers targeted to a particular impact.

In order to test this idea, we sampled macroinvertebrates from rivers in south-eastern Australia subjected tothree different types of anthropogenic disturbance: operation of large dams, discharge of effluent from municipalsewage treatment plants, and contamination by metals originating from historical mining. Using macroinvertebratedata from sampling sites with varying levels of exposure to disturbance, we developed sensitivity numbers formacroinvertebrate families for individual rivers and combinations of rivers with the same disturbance type.

Sensitivity numbers calculated for individual families differed significantly according to disturbance type inseveral cases. Gastropodmolluscs (family Thiaridae) were tolerant of dam effects but sensitive to sewage and metals,whereas coenagrionid damselfly nymphs, elmid beetles and ostracods were most tolerant of sewage. Corydalidalderfly larvae, leptophlebiid mayfly nymphs, lestid damselfly nymphs, libellulid dragonfly nymphs and scirtidbeetle larvae were most tolerant of metals. Indices constructed using sensitivity numbers for a particular disturbancewere generally most responsive to that disturbance, but there was considerable generality in responses as well assubstantial variability between different rivers with the same disturbance type. In particular, macroinvertebratecommunities at sites downstream of dams responded quite variably, probably because of substantial differences inrelease regimes. We conclude that the approach has merit but requires considerable further development and testing,as well as consideration of the levels of specificity and diagnostic strength that are appropriate or achievable.

Introduction

In many parts of the world biotic indices are applied tostream macroinvertebrate community data in order todetect and monitor water pollution and other forms ofhuman impact. They include the Biological Monitor-ing Working Party (BMWP) score system used in GreatBritain (Armitage et al., 1983), Stark’s (1985) NewZealand Macroinvertebrate Community Index (MCI),Hilsenhoff’s (1987, 1988) Wisconsin Biotic Index (BI)and Family Biotic Index (FBI), the Spanish Biolog-

ical Monitoring Water Quality (BMWQ) score sys-tem (Camargo, 1993), the South African Score System(SASS) of Chutter (1995) and the Australian SIGNALindex (Chessman, 1995). Indices of this type are calcu-lated by assigning numerical values to individual taxa(species, genera or families) that reflect their inferredsensitivities or tolerances, and then summing or aver-aging the values for all taxa or individuals in a sample.

Such indices are increasingly popular because theyare responsive to different types of anthropogenicimpact (Pinder & Farr, 1987b; Barton & Metcalfe-

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Smith, 1992; Camargo, 1993; Resh & Jackson, 1993;Growns et al., 1995), robust to variations in samplesize (Armitage et al., 1983; Pinder & Farr, 1987a;Stark, 1993; Growns et al., 1997), and have low vari-ability both within a site and over time (Jones et al.,1981; Barton & Metcalfe-Smith, 1992; Hannaford &Resh, 1995). They are also subject to continuing eval-uation and refinement (Cao et al., 1996, 1997; Walley& Hawkes 1996, 1997; Chessman et al., 1997).

A particular difficulty in index construction is thatindividual taxa may not be equally sensitive to all typesof anthropogenic disturbance. For example, laboratorystudies show that particular species of aquatic macroin-vertebrates vary quite widely in their tolerances of spe-cific pollutants (Chapman et al., 1982; Sloof, 1983;Ewell et al., 1986). Similarly, the abundances of par-ticular macroinvertebrate taxa can differ greatly amongstreams affected by flow alteration and various typesof land-use activities and wastewater discharges (Yoder& Rankin, 1995). In these circumstances, it is difficultto assign representative sensitivity values to individualtaxa.

The Australian biotic index SIGNAL (StreamInvertebrate Grade Number – Average Level) wasdeveloped by Chessman (1995) from information ina relatively small number of published studies. Thesestudies covered streams in eastern Australia disturbedby biodegradable organic pollutants such as sewageand sugar mill wastewater (Campbell, 1978; Arthing-ton et al., 1982; Watson et al., 1982; Pearson & Pen-ridge, 1987; Cosser, 1988), trace metals derived frommining activities (Nicholas & Thomas, 1978; Norris etal., 1982; Norris, 1986; Mackey, 1988), sedimentationresulting from land clearing and urban development(Hogg & Norris, 1991) and power station and pulpmill effluents (Marchant et al., 1984; Chessman &Robinson, 1987). The sensitivity grade numbers formacroinvertebrate families used in the original ver-sion of SIGNAL (SIGNAL-95) range from 10 (mostsensitive) to 1 (most tolerant) and represent a subjec-tive compromise between responses to a wide rangeof types of disturbance. The index is calculated as aweighted or unweighted mean of the grades of familiespresent in a standard sample.

SIGNAL-95 was tested in the Nepean River, BlueMountains and Sydney regions of New South Wales(Growns et al., 1995, 1997), where it showed a highdegree of sensitivity to sewage pollution and salinityand little response to gradients in natural factors suchas stream size and elevation. Subsequently, Chessmanet al. (1997) developed a modified version of SIGNAL

for the Hunter River basin, New South Wales, usingan iterative procedure similar to reciprocal averaging(Hill, 1973) to assign basin-specific grade numbersobjectively. The modified index (SIGNAL-HU97) washighly correlated with water turbidity and electricalconductivity, as well as with altitude and the conditionof the stream banks and bed.

In this paper we explore the issue of developinga suite of biotic indices targeted to specific types ofdisturbances. Each index would be constructed usingseparate grade numbers reflecting sensitivities to a par-ticular disturbance. By calculating values of each indexfor a study site, it would then be possible to obtain notonly an indication of the degree of impact, but alsoa diagnosis of the particular type of impact. To testthis idea we sampled rivers affected by three well-studied types of disturbance: large dams, municipalsewage effluent, and trace metals originating from his-torical mining. We used the approach of Chessman etal. (1997) to derive grade numbers for each river andfor combinations of all rivers subjected to a particulardisturbance type, compared grade numbers for differ-ent disturbances and tested the ability of grades derivedfrom one river to interpret impacts on other rivers.

Study areas

Site selection

Three river systems subjected to each type of distur-bance where chosen on the basis of published studies,unpublished data and consultation with managementagencies and researchers. Rivers were selected fromthe northern, central and southern sections of south-eastern mainland Australia (Table; Figure 1). Withineach river system, five sites were selected for rapidmacroinvertebrate sampling: three sites downstreamof the disturbance of interest and two reference sitesunaffected by that disturbance, though not necessar-ily pristine (Appendix 1). Information from previousstudies was used to select the downstream sites so thatthey represented successively lower levels of impact.Reference sites were located upstream of the point ofdisturbance or on a tributary and were similar to impactsites in altitude, gradient and stream size.

Dams

Barambah Creek (Queensland) has been impounded tostore irrigation water and receives an interbasin trans-

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Figure 1. Map of south-eastern Australia showing the locations of the study rivers.

fer of water for power generation. The main impound-ment is Bjelke-Petersen Dam on Barker Creek, themajor tributary. Several weirs have been erected acrossBarambah Creek to permit operation of irrigation sys-tems. The upper catchment has a vegetation coverof irrigated crops and pasture (Bluhdorn & Arthing-ton, 1994). The Severn River (New South Wales) isimpounded by Pindari Dam to supply water for irriga-tion. The catchment is vegetated by a mixture of nativeforest on the slopes and pasture on more gently slop-ing land, and includes some small mines. The ThomsonRiver (Victoria) drains a steep, forested catchment andis impounded by the Thomson Dam to harvest water

for metropolitan Melbourne (Gippel & Stewardson,1995).

Sewage

Originating within the city of Toowoomba, where itreceives secondary sewage effluent and urban runoff,Gowrie Creek (Queensland) flows across plains usedextensively for crop and beef production (Cosser,1988). The Nattai River (New South Wales) has acatchment predominantly covered by native forest. Itsupper reaches receive secondary municipal effluent andrunoff from urban and industrial areas of Mittagongand surrounds (Boyd, 1993). Yarrowee Creek (Victo-

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Table 1. Details of rivers in study.

Disturbance River and state References consulted for site selection No. of habitats

sampled per site

(mean and range)

Survey 1 Survey 2

Dam Barambah Creek, Queensland Bluhdorn & Arthington (1994) 2.4 2.0

(1–3) (0–3)

Dam Severn River, New South Wales NSW Department of Land and Water 3.4 3.6

Conservation monitoring data (3–4) (3–4)

Dam Thomson River, Victoria Doeg et al. (1987); Marchant (1989) 3.8 2.6

(3–4) (0–4)

Sewage Gowrie Creek, Queensland Cosser (1988) and Qld Department of 3.4 2.6

Environment and Heritage monitoring data (2–5) (0–5)

Sewage Nattai River, New South Wales Boyd (1993) 3.8 3.4

(3–4) (3–4)

Sewage Yarrowee Creek, Victoria Central Highlands Water recent 2.4 2.8

monitoring data (2–3) (2–3)

Metals River Dee, Queensland Mackey (1988) 2.6 2.4

(1–4) (1–3)

Metals Daylight Creek, New South Wales Napier (1992) 3.2 3.2

(3–4) (3–4)

Metals Molonglo River, New South Weatherly et al. (1967); Norris (1986) 3.4 3.0

Wales/Australian Capital Territory (3–4) (3)

ria) flows through the city of Ballarat, and receivestertiary sewage effluent. Land uses in its catchmentinclude agriculture, forestry and gold mining. Mineshaft seepage is periodically discharged into the creek(G. Cramer, Central Highlands Water, personal com-munication).

Mining

The River Dee (Queensland) receives acidic seepagefrom an open cut gold and silver mine (Mackey, 1988).The predominant land uses in its catchment are graz-ing and cropping, with forest present on the ranges.Daylight Creek (New South Wales) has a catchmentof shales and quartizites including sulphide ores thathave been mined for gold, silver, copper and lead in acomplex of mine workings, but were effectively aban-doned last century (Chapman et al., 1983). Much of thecatchment supports native forest, and there are someareas of agricultural land, forest plantations and urbansettlement. In its upper reaches, the Molonglo River(Australian Capital Territory and New South Wales)flows beside the former Captains Flat Mine. Copper,gold, lead, pyrite, silver and zinc were mined therefrom shale bands and clays between 1882 and 1962

(Dames & Moore, 1993). The river flows from hillsvegetated by native woodland onto a broad floodplain,which has been largely cleared of woody vegetationfor grazing.

Materials and methods

Macroinvertebrate sampling and identification andenvironmental measurements

Sampling took place between November 1994 and Sep-tember 1995. Each river system was sampled twice, atan interval of approximately five months; dates arelisted in Appendix 1. Collections were obtained fromriffles, pool edges, rocks in pools, submerged wood andbeds of submerged or emergent aquatic macrophytes,using dip nets and hand picking (Chessman, 1995).A single collection was obtained from each habitatpresent at each site on each occasion, but particularhabitats were missing at many sites (Appendix 1). Aprolonged drought in northern sections of the studyregion resulted in fewer sites and habitats being sam-pled in the second survey (Table 1), and heavy snowin September prevented access to one Thomson River

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site. Representative specimens from each habitat werelive- picked on site into 70% ethanol for at least 30 minand until no new taxa were observed, or 60 min hadelapsed, whichever was the sooner.

Macroinvertebrate samples were returned to thelaboratory where specimens were identified and count-ed using stereomicroscopes. Identification was gener-ally taken to family level. Chironomid larvae wereidentified to sub-family. Opisthopora (Oligochaeta),Nematoda, Nemertea and Ostracoda were not taken tofamily level.

Calculation of sensitivity grade numbers

Sensitivity grade numbers for each macroinvertebratetaxon were derived initially for each combination ofriver system, habitat and season. The river systemswere analysed separately so that we could comparegrades within and among disturbance types. The habi-tats were analysed one by one because they often hadvery different macroinvertebrate faunas, even at thesame site. Likewise, the seasons were treated individ-ually so that the analyses would not be confounded bytemporal changes in the fauna or the degree of distur-bance at each site. The procedure used to derive gradenumbers was an iterative algorithm similar to that ofChessman et al. (1997). For each data set, the refer-ence sites in each river system were set to initial indexvalues of 10, and the sites immediately downstream ofthe point of impact, farther downstream and farthestdownstream were respectively set to 0, 3.3 and 6.7.Pearson’s correlation coefficients were then calculatedbetween the abundances of the various macroinver-tebrate taxa and the site values. The taxon with thehighest positive correlation, and therefore presumed tobe the most sensitive to the disturbance, was assignedan initial grade number of 10. The taxon with the low-est negative correlation, and therefore presumed to bethe most tolerant, was awarded an initial grade numberof 1. The remaining taxa were awarded intermediategrades scaled in proportion to their respective correla-tion coefficients.

New index values were then calculated for eachsite as a weighted mean of the initial grades of thetaxa present. Weighting was according to the abun-dance of each taxon. The correlation procedure wasthen repeated, new grade numbers were assigned, andrevised index values were calculated. This routine wasrepeated a further eight times, after which the gradenumbers and site index values had stabilised; the Pear-son correlation between successive sets of grades at

Table 2. Summary of analysis of variance for macroinvertebratetaxa with significantly different pollution sensitivity grade num-bers for different disturbance types.� P<0.05;�� P<0.01.

Taxon F statistic Mean grade number

Dam Sewage Metals

Coenagrionidae 7.6� 8 3 5

Corydalidae 100.1�� 9 7 2

Elmidae 15.4� 9 6 7

Leptophlebiidae 15.0� 9 9 7

Lestidae 21.2� – 9 6

Libellulidae 24.2�� 7 7 2

Ostracoda 11.2�� 7 3 8

Scirtidae 7.2� 8 6 3

Thiaridae 219.7� 3 8 9

this point was always greater than 0.98 and generallygreater than 0.999.

Weighted means of the grade numbers for the vari-ous combinations of habitat and season were calculat-ed in order to award each taxon an overall grade foreach river system. Weighting was done according tothe number of specimens per taxon collected in eachhabitat in each season. Mean grades were then rescaledso that they ranged from 1 to 10.

The specificity and generality of the derived grades,both within a disturbance type and among disturbancetypes, were tested in several ways. Firstly, analysisof variance was used to determine whether the gradenumbers calculated for each family for different riversvaried significantly according to the disturbance type.Secondly, weighted index values were calculated foreach sample using the grade numbers for the riverfrom which it came (‘local grades’) and for the oth-er two rivers with the same type of disturbance (‘sis-ter grades’). Pearson correlation coefficients betweenlocal and sister index values were examined for signif-icance.

Finally, overall grade numbers were calculated foreach disturbance type. In this case the algorithm wasapplied to data sets combined across all three riversfor each combination of habitat and season. Weightedaverage grades were derived in the same manner asfor the analysis of data from individual rivers. Weight-ed index values were then calculated for each sam-ple using the overall grades for each disturbance type.Pearson correlations between dam (SIGNAL-DAM),sewage (SIGNAL- SEW) and metals (SIGNAL-MET)index values were tested for significance.

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Table 3. Pearson correlations betweena priori (AP) SIGNAL index values for all samples for each riverand index values derived using grade numbers derived for that river and for the other two rivers with thesame disturbance type. For example, for Gowrie Creek, AP & 1 represents the correlation betweenapriori Gowrie Creek index values and Gowrie Creek index values calculated using Gowrie grades; 2 & 3represents the correlation between Gowrie Creek index values calculated using Nattai grades and GowrieCreek index values calculated using Yarrowee grades.� P<0.05;�� P<0.01; significant correlationsare shown in bold.

Disturbance River Correlation

AP & 1 AP & 2 AP & 3 1 & 2 1 & 3 2 & 3

Dam Barambah (1) 0.73�� 0.23 0.45� 0.58�� 0.41� 0.31

Severn (2) 0.26 0.54�� - 0.03 0.57�� - 0.16 - 0.13

Thomson (3) - 0.08 0.20 0.68�� 0.51�� - 0.38� - 0.01

Sewage Gowrie (1) 0.24 0.34 0.45� 0.36� 0.34 0.36�

Nattai (2) - 0.49�� 0.90�� 0.76�� - 0.36� - 0.32� 0.83��

Yarrowee (3) - 0.07 0.75�� 0.79�� - 0.21 - 0.03 0.79��

Metals Dee (1) 0.67�� 0.40� - 0.03 0.35 0.06 - 0.13

Daylight (2) 0.30 0.39� - 0.01 0.69�� - 0.23 - 0.13

Molonglo (3) 0.06 0.42� 0.72�� 0.25 0.13 0.62��

Results

Ninety-one taxa occurred in enough rivers to enabletesting of significant differences in grade numbersderived for individual rivers. Of these, only nine hadgrade numbers that differed significantly (P<0.05)between disturbance types (Table 2). This is abouttwice as many as expected by chance. Gastropodmolluscs of the family Thiaridae were tolerant ofdam effects (grade number of 3) but sensitive tosewage and metals (grade numbers of 8 and 9 respec-tively). On the other hand, coenagrionid damselflynymphs, elmid beetles and ostracods were least sensi-tive to sewage. Alderfly larvae (Megaloptera: Corydal-idae), leptophlebiid mayfly nymphs, lestid damselflynymphs, libellulid dragonfly nymphs and scirtid beetlelarvae were all least sensitive to metals (Table 2).

When index values were derived for each sampleusing local grades and sister grades, some interestingresults were apparent (Table 3). Correlations betweenindex values calculated using the derived grades andthe initial index values (of 0, 3.3, 6.7 and 10) were sta-tistically significant in over 50% of instances. Theseincluded over a third of cases where sister grades wereused. In other words, the index values generally reflect-ed oura priori assignment of impact levels, based onprior information, and often did so even when theywere calculated using grade numbers derived quiteindependently for another river with the same distur-bance type. In addition, there was a high frequencyof significant correlations between index values calcu-

lated for a particular river using local grades and indexvalues for the same river calculated using sister grades(Table 3). This is further evidence that the grade num-bers were to some degree transferrable from one riverto another with a similar type of disturbance.

Overall grade numbers derived using combineddata from all rivers with a particular disturbance typeare listed in Table 4, rounded to the nearest whole num-ber. Grades calculated for taxa represented by fewerthan 10 specimens in the data set concerned are notlisted, because of their doubtful reliability (cf. Chess-man et al., 1997). For over a third of the taxa in Table4, the range of grades among disturbances was twounits or more. Correlations between index values cal-culated using the relevant overall grades (for eitherdams, sewage or metals) anda priori index valueswere statistically significant in two thirds of instances(Table 5; Figure 2). However, when index values werecalculated using overall grades for the wrong type ofdisturbance, they still correlated significantly with thea priori grades in almost 50% of instances. Similarly,index values calculated using the grades for the relevantdisturbance correlated significantly with index valuescalculated using grades for the other disturbances in50% of cases. Thus grade numbers were to a degreetransferrable even between disturbance types, as wellas among rivers within the same disturbance type.

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Table 4. Original (SIGNAL-95) and revised (SIGNAL-DAM,SIGNAL-SEW and SIGNAL-MET) sensitivity grade numbersfor common macroinvertebrate taxa.

Taxon SIGNAL- SIGNAL- SIGNAL- SIGNAL-

95 DAM SEW MET

grade No. grade No. grade No. grade No.

Aeshnidae 6 7 9 3

Ancylidae 6 6 2 8

Aturidae 7 4

Atyidae 6 6 9 8

Baetidae 5 7 9 7

Belostomatidae 5 9

Caenidae 7 8 7 7

Calamoceratidae 8 6 8 8

Calocidae 8 6

Ceinidae 5 5 7

Ceratopogonidae 6 7 8 4

Chironomidae

(Chironominae) 6 6 4

Chironomidae

(Orthocladiinae) 6 4 5

Chironomidae

(Podonominae) 8 8 7

Chironomidae

(Tanypodinae) 7 7 6

Cirolanidae 5

Coenagrionidae 7 8 6 3

Coloburiscidae 10 9

Conoesucidae 8 7 7

Corbiculidae 6 2 3

Corduliidae 7 4 10

Corixidae 5 10 3 4

Corydalidae 4 8 9 1

Culicidae 2 7 9 3

Dixidae 8 7

Dugesiidae 3 4 2 3

Dytiscidae 5 6 6 4

Ecnomidae 4 5 8 10

Elmidae 7 7 8 8

Erpobdellidae 3 1

Gerridae 4 7 7 5

Glossiphoniidae 3 2 3 4

Glossosomatidae 8 9

Gomphidae 7 7 9 5

Gripopterygidae 7 9 9 8

Gyrinidae 5 3 4

Hebridae 6 6

Helicophidae 10 8

Helicopsychidae 10 5 7

Hydraenidae 7 5 6 7

Hydridae 4 4

Hydrobiidae 5 3 3

Table 4. Continued.

Taxon SIGNAL- SIGNAL- SIGNAL- SIGNAL-

95 DAM SEW MET

grade No. grade No. grade No. grade No.

Hydrobiosidae 7 8 9

Hydrochidae 7 8 6 7

Hydrodromidae 6

Hydrometridae 5 2

Hydrophilidae 5 6 8 6

Hydropsychidae 5 8 2 7

Hydroptilidae 6 6 6 7

Hygrobatidae 7 8 7

Isostictidae 7 7

Leptoceridae 7 7 10 8

Leptophlebiidae 10 7 9 8

Lestidae 7 5 6

Libellulidae 8 6 6 1

Lumbriculidae 1 2

Lymnaeidae 3 7 6 7

Megapodagrionidae 7 7

Mesoveliidae 4 5 4

Naididae 1 6 2 6

Naucoridae 5 8 8

Nematoda 4 6 5

Notonectidae 4 9 10 6

Notonemouridae 8 5 1

Odontoceridae 8 5

Ostracoda 4 2 8

Oxidae 6

Palaemonidae 4 7

Paramelitidae 2

Parastacidae 7 6 5

Philopotamidae 10 7 9 6

Physidae 3 5 4 5

Planorbidae 3 5 7 6

Pleidae 5 5

Polycentropodidae 8 4 10

Protoneuridae 7 1

Psephenidae 5 4 6 8

Ptilodactylidae 6

Pyralidae 6 5

Scirtidae 8 7 9 3

Simuliidae 5 8 4 10

Sphaeriidae 6

Spongillidae 3 4

Stratiomyidae 2

Temnocephalidae 7 9 6

Thiaridae 7 5 6 5

Tipulidae 5 7 8 6

Tubificidae 1 2 4 6

Unionicolidae 9 7

Veliidae 4 8 6 9

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Discussion

To be effective, a diagnostic biotic index needs two keyproperties. Firstly, its values should show a strong rela-tionship with the degree of exposure of a macroinverte-brate community to the particular disturbance at whichit is targeted. Secondly, for a particular type of impact,the relevant diagnostic index should assume a moreextreme value than non-relevant diagnostic indices.Forexample, in a river affected by sewage disposal but notby dams or metal pollution, the sewage index would beexpected to assume lower values than either the damor metals index.

The dam index (SIGNAL-DAM) performed ratherpoorly on both criteria. Although it produced indexvalues that correlated significantly witha priori indexvalues when calculated using grades derived for thesame river (local grades), it only once produced asignificant correlation when calculated using gradesderived for a different dammed river (sister grades).Barambah Creek index values calculated using Thom-son River grades were significantly correlated withapriori Barambah Creek index values (Table 3). Whenoverall grades were used, only Barambah Creek indexvalues had a significant correlation witha priori indexvalues ( Table 5). Worse, in only two of nine instanceswere mean SIGNAL-DAM index values lower thanboth mean SIGNAL-SEW and mean SIGNAL-METindex values for dam-affected sites (Figure 2). This isfewer than the three instances in nine expected purelyby chance.

The sewage index had the best performance of thethree. There were only two cases (of a possible six) inwhich it produced sister index values that did not cor-relate significantly witha priori index values (Table3). However, the grades derived for Gowrie Creek pro-duced index values for Gowrie Creek itself and forYarrowee Creek that did not correlate significantly witha priori index values. For the Nattai River, index valuescalculated using Gowrie Creek grades were negativelycorrelated witha priori index values.

When overall sewage grades were used, both Nat-tai River and Yarrowee Creek index values had high-ly significant correlations witha priori index values(Table 5), but there was again no significant correla-tion for Gowrie Creek. However, in all nine instancesmean SIGNAL-SEW index values were lower thanboth mean SIGNAL-DAM and mean SIGNAL-METindex values for sewage-affected sites (Figure 2).

The performance of the metals index was mixed.In four of six instances it produced sister index val-

ues that did not correlate significantly witha prioriindex values. In particular, Daylight Creek index val-ues did not correlate significantly witha priori indexvalues when calculated using grades derived for eitherthe River Dee or the Molonglo River (Table 3). How-ever, when overall grades were used there was a sig-nificant correlation witha priori index values for allthree metals-affected rivers (Table 5). In six of nineinstances, mean SIGNAL-MET index values were low-er than both mean SIGNAL-DAM and mean SIGNAL-SEW index values for metals- affected sites (Figure 2),twice the proportion expected by chance.

A number of factors may have influenced the per-formance of the indices. The lack of diagnostic powerand generality in the dam index may be at least partlyrelated to differences in the flow regimes downstreamof the three dams. The dams on Barambah Creek andthe Severn River are used primarily to store waterfor downstream release during the irrigation season,whereas the Thomson Dam is used to divert water tothe Melbourne metropolitan supply by pipeline. Themain changes from the natural flow regime of Baram-bah Creek has been a reduction in the size of peakflows and a change in their timing, and an increase inthe duration of very low flows (Bludhorn & Arthing-ton, 1994). Similar flow changes have occurred in theSevern River below Pindari Dam, but following thecompletion of storage enlargement an interim environ-mental flow regime was implemented in April 1995(i.e. between our first and second surveys; Ross et al.,1996). This has much reduced the persistence of verylow flows. In the Thomson River below the ThomsonDam there is a continual environmental flow alloca-tion, which varies seasonally, plus fluctuating releasesthrough a hydroelectric plant. This has greatly reducedthe variability in total daily flows compared with thenatural regime (Gippel & Stewardson, 1995).

Environmental differences among streams dis-turbed by sewage may also have influenced the calcu-lation. Streams in the Gowrie Creek system have beengreatly affected by agricultural activities and associat-ed bank damage and fouling by stock, increased ero-sion and loss of riparian vegetation, so that their overallenvironmental condition is generally poor (Phillips &Moller, 1995). Thus the effects of sewage disposal mayhave been confounded by other factors, preventing thederivation of grades fully targeted on sewage. Familydiversity was low at all sites in this system, even thereference sites. The apparent severity of agriculturalimpacts in this system suggests that it would be useful

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Table 5. Pearson correlations betweena priori (AP) SIGNAL index values for all samples for each river and indexvalues derived using overall grade numbers derived for each disturbance type (DAM, SEWage and METals). Forexample, for Gowrie Creek, AP & DAM represents the correlation betweena priori Gowrie Creek index valuesand Gowrie Creek index values calculated using dam grades; SEW & MET represents the correlation betweenGowrie Creek index values calculated using sewage grades and Gowrie Creek index values calculated using metalsgrades.� P<0.05;�� P<0.01; significant correlations are shown in bold.

Disturbance River Correlation

AP & DAM AP & SEW AP & MET DAM & SEW DAM & MET SEW & MET

Dam Barambah (1) 0.50�� 0.28 0.45� 0.73�� 0.37 0.23

Severn (2) 0.23 0.38� 0.49�� 0.24 - 0.05 0.47��

Thomson (3) - 0.01 0.49�� - 0.29 - 0.23 0.08 - 0.32

Sewage Gowrie (1) 0.27 0.24 - 0.03 0.24 - 0.15 - 0.45�

Nattai (2) 0.62�� 0.90�� 0.60�� 0.60�� 0.45�� 0.68��

Yarrowee (3) 0.36 0.80�� 0.43� 0.51�� 0.45� 0.59��

Metals Dee (1) - 0.56�� - 0.15 0.73�� 0.15 - 0.46� 0.10

Daylight (2) - 0.02 0.29 0.36� 0.70�� 0.61�� 0.69��

Molonglo (3) 0.15 0.15 0.59� 0.36� 0.08 0.08

to develop an index targeted on agricultural disturbanceof streams.

Few environmental differences were apparentamong the streams affected by metals. All three sys-tems are contaminated by acidic seepage from min-ing spoil, which contains elevated concentrations ofcadmium, copper, lead and zinc, although the rela-tive proportion of each metal varies somewhat betweensystems (Norris, 1986; Mackey, 1988; Napier, 1992).However, stream substratum concretion by metal salts,which reduces macroinvertebratehabitat (Davies et al.,1996), was observed only on the bed of the River Dee.

Variation in levels of tolerance among members ofa family is another factor that could influence indexperformance. Grade numbers were derived at the fam-ily level because this level is applicable across a widegeographic range. Comparison of grades between riversystems would have been difficult at the species levelbecause relatively few species would be common tomost of the rivers in our study. However, the deriva-tion of family grades raises the question of whethersensitivity can be generalised adequately at the familylevel. Family-level grades are used for several bioticindices (Armitage et al., 1983; Camargo, 1993; Chess-man, 1995; Chutter, 1995), but there are often substan-tial differences in tolerance between species withina family (Resh & Unzicker, 1975). Published toler-ance values for species and genera of North Americanfreshwater macroinvertebrates vary quite widely with-in families, though generally not as much as betweenfamilies (see Hilsenhoff, 1987; Lenat, 1993; DeShon,1995). Hilsenhoff (1988) showed that a family-level

biotic index resulted in a loss of resolution comparedwith an index at the species/genus level, although itwas suitable for initial rapid screening of study sites.It would be desirable to extend the diagnostic indexapproach to the species or genus level in the future inorder to increase its sensitivity and reliability.

The paucity of significant differences in gradesaccording to the type of disturbance may partly reflectthe low power of the analysis, since grades werederived for a maximum of only three rivers per dis-turbance type. In addition, only 21 taxa were collectedin all nine river systems, and many of the grade deriva-tions were based on fewer than 10 specimens. Alterna-tively, it may be that many families are about equallytolerant to a wide range of disturbances. This couldalso be partly a result of the variations in tolerances ofspecies within a family.

The families that had a high grade number (9 or 10)for dams were Belostomatidae, Coloburiscidae, Corix-idae, Glossosomatidae, Gripopterygidae, Notonecti-dae and Unionicolidae (Table 5). Consistent with this,Marchant (1989) noted a reduction in the abundance ofColoburiscoidessp. (Coloburiscidae) below the Thom-son Dam. The derivation of high grades for hemipter-an families (Belostomatidae, Corixidae and Notonec-tidae) is surprising, however, since these are normallyassociated with still waters and would be expected to befavoured by the general reduction in flow downstreamof dams. Families with a very low grade number fordams (1 or 2) were Corbiculidae, Erpobdellidae, Glos-siphoniidae, Protoneuridae and Tubificidae. Marchantet al. (1984) recorded abundant populations ofCor-

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bicula australisdownstream of a dam on the La TrobeRiver, Victoria. In Great Britain, Armitage et al. (1987)found that the Erpobdellidae was among a group offamilies that was more abundant downstream of damsthan was predicted using data from unregulated rivers.

For sewage there were many highly intolerant fam-ilies with grade numbers of 9 or 10: Aeshnidae, Atyi-dae, Baetidae, Corduliidae, Corydalidae, Culicidae,Gomphidae, Gripopterygidae, Hydrobiosidae, Lepto-ceridae, Leptophlebiidae, Notonectidae, Philopotami-dae, Polycentropodidae, Scirtidae and Temnocephali-dae. The sensitivity of many of these families to organ-ic pollution in Australia has been noted by severalauthors. For example, Campbell (1978) found lep-tophlebiid mayflies to be sensitive to organic pollu-tion in Dandenong Creek, Victoria, and McIvor (1976)observed that baetid and leptophlebiid mayfly nymphs,corydalid alderfly larvae and gripopterygid stoneflynymphs were absent downstream of a sewage outfallin Moggill Creek, Brisbane, during low-flow periods.The sensitivity of corduliid and gomphid dragonfliesto sewage in Bulimba Creek, Quensland, was report-ed by Watson et al. (1982). Marchant et al. (1984)illustrated the sensitivity of families such as Baetidae,Gripopterygidae, Leptophlebiidae and Scirtidae to thedischarge of treated sewage and other disturbances inthe La Trobe River, Victoria.

The tolerant taxa with sewage grades of 1–2were Ancylidae, Dugesiidae, Hydropsychidae, Lum-briculidae, Naididae, Ostracoda and Paramelitidae.Hydropsychids and naidids are considered moderate-ly tolerant of organic pollution (Hawkes, 1962). Jolly& Chapman (1966) noted thatLumbriculustoleratessewage pollution but considered that it favoured recov-ery zones. However they suggested that the Ancylidaeare intolerant of sewage pollution.

The families with grade numbers of 9–10 for metalswere the Ecnomidae, Simuliidae and Veliidae. Consis-tent with this, Mackey (1988) found a reduced abun-dance of veliids immediately downstream of the pointof contamination of the River Dee. However, Norriset al. (1982) foundEcnomussp. andAustrosimuliumsp. to be abundant at metals-contaminated sites in theSouth Esk River, Tasmania, although simuliid numberswere reduced immediately downstream of the sourceof contamination. Napier (1992) also found ecnomidand simuliid larvae and veliids at metals-contaminatedsites in the Daylight Creek system. These observationsprovide further evidence of the difficulty in generalis-ing responses to metals, at least at the family level.

The Corydalidae, Hydrometridae, Libellulidae andNotonemouridae had metals grades of 1–2. Napi-er (1992) consistently recordedArchichauliodessp.(Corydalidae) downstream of mine works at SunnyCorner. Mackey (1988) found libellulid dragonflies atmost sites downstream of the historical mining areaon the River Dee, but they were found only at a sitewell downstream of the Sunny Corner mines (Napi-er, 1992). There is evidence of metal tolerance bynotonemourid stoneflies in the studies of Nicholas andThomas (1978), Norris et al. (1982) and Norris (1986).

It is interesting to note that the normally very sen-sitive mayfly family Leptophlebiidae had a significant-ly lower grade number for metals than for dams orsewage (Table 2). Lake et al. (1977) noted that nymphsof Atalophlebioides(Leptophlebiidae) appeared to bepartly tolerant of metal pollution in the King Riversystem, Tasmania.

Conclusions

It appears from this study that the development of diag-nostic indices is a realistic possibility, at least for sometypes of disturbance. However, differences in sensi-tivities to particular pollutants among species withina family may necessitate the derivation of grade num-bers at the species or possibly the genus level. Becauseof the sporadic occurrence of most macroinvertebratetaxa, and differences in responses between river sys-tems, large amounts of data will be required to cal-culate reliable grades. There is also uncertainty aboutthe number of different types of disturbance for whichseparate grading systems will need to be developed. Itis probable that for disturbances such as organic pol-lution, a high degree of generality exists. However, forother disturbances such as flow alteration, it is likelyto be necessary to differentiate a number of types ofdisturbance regimes. Even then, a lack of generalityin response (cf. Castella et al., 1995) may prevent thedevelopmentof adequate diagnostic systems, and otherapproaches may be required.

Acknowledgements

This work was partly funded by the Land and WaterResources Research and Development Corporationunder the National River Health Program, Monitor-ing River Health Initiative, with the remaining fundingcontributed by the Sydney Water Corporation.

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Many people provided data and advice for siteselection: Satish Choy and Peter Thompson, WaterResources, Queensland Department of Primary Indus-tries; Phil Cosser and Andrew Moss, QueenslandDepartment of Environment and Heritage; Mick Balesand Meredith Royal, New South Wales Department ofLand and Water Conservation; Richard Norris, Uni-versity of Canberra; Derek Rutherford, New SouthWales Environment Protection Authority; GillianNapier, CSIRO Division of Water Resources; San-dra Sdraulig, Monash University; Kent Hortle, Envi-ronmental Management and Assessment; Tim Doeg,Victorian Department of Conservation and NaturalResources; Richard Marchant, Museum of Victoria;Melbourne Water Corporation, Woori Yallock region;John Saunders, Australian Water Technologies; GeoffCramer, Central Highlands Water; Tim Fletcher, Uni-versity of Melbourne.

The contributions of David Ross to field samplingand reference compilation are gratefully acknowl-edged. Simon Williams also assisted with sampling.Cheryl Patterson is thanked for data entry.

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Appendix 1. Details of sampling locations, habitats and dates. For drainages where the main stream changed name after meeting asmaller tributary, the convention of imposing the mainstream name for sites downstream of that tributary was followed. E, pool edge;M, aquatic macrophytes; P, pool rocks; R, riffle; W, wood.

River Site description Latitude Longitude First survey Second survey

(S) (E) Date E M P R W Date E M P R W

Barambah Barambah Creek at Ficks Crossing 26:15 151:53 23 Apr. 95 + 27 Sep. 95 + +

Barambah Barambah Creek at Joe Sippel Weir 26:16 152:00 23 Apr. 95 + + + 27 Sep. 95 + + +

Barambah Barambah Creek at Pei Road 26:20 152:13 24 Apr. 95 + + –

Barambah Barambah Creek d/s Barker Creek 26:18 151:58 23 Apr. 95 + + + 27 Sep. 95 + + +

Barambah Boonara Creek 26:01 152:07 24 Apr. 95 + + + 26 Sep. 95 + +

Severn Beardy River at Bruxner Highway 29:11 151:22 15 Mar. 95 + + + + 23 Aug. 95 + + +

Severn Severn River 800 m d/s Pindari Dam 29:25 151:14 14 Mar. 95 + + + 22 Aug. 95 + + + +

Severn Severn River at Macintyre Falls Road 29:10 150:59 15 Mar. 95 + + + 23 Aug. 95 + + +

Severn Severn River at Strathbogie 29:29 151:28 16 Mar. 95 + + + 23 Aug. 95 + + + +

Severn Severn River at Wells Crossing 29:22 151:08 15 Mar. 95 + + + + 22 Aug. 95 + + + +

Thomson Aberfeldy River at Walhalla Rd 37:51 146:25 3 Apr. 95 + + + + 5 Sep. 95 + + + +

Thomson Thomson River 1 km d/s Thomson Dam 37:51 146:24 4 Apr. 95 + + + + 4 Sep. 95 + + + +

Thomson Thomson River 9.5 km d/s Thomson Dam 37:52 146:24 4 Apr. 95 + + + + 5 Sep. 95 + +

Thomson Thomson River at Brunton Bridge 38:01 146:27 5 Apr. 95 + + + 5 Sep. 95 + + +

Thomson Thomson River at Woods Point Road 37:42 146:15 4 Apr. 95 + + + + –

Gowrie Gowrie Creek 1 km d/s STP 27:30 151:53 25 Apr. 95 + + 28 Sep. 95 + +

Gowrie Gowrie Creek 54 km d/s STP 27.24 151:34 27 Apr. 95 + + + + 28 Sep. 95 + + +

Gowrie Gowrie Creek 79 km d/s STP 27:20 151:27 27 Apr. 95 + + + + + 28 Sep. 95 + + + + +

Gowrie Oakey Creek 27:25 151:45 27 Apr. 95 + + –

Gowrie Westbrook Creek 27:38 151:49 25 Apr. 95 + + + + 28 Sep. 95 + + +

Nattai Gibbergunyah Creek at ford 34:26 150:27 23 Nov. 94 + + + + 12 Jul. 95 + + +

Nattai Little River at Fire Road 34:12 150:28 23 Nov. 94 + + + + 11 Jul. 95 + + + +

Nattai Nattai River at Starlights Trail 34:18 150:22 15 Dec. 94 + + + + 16 Aug. 95 + + +

Nattai Nattai River at The Crags 34:23 150:26 25 Nov. 94 + + + + 12 Jul. 95 + + + +

Nattai Rocky Waterholes Creek 34:26 150:24 24 Nov. 94 + + + 15 Aug. 95 + + +

Yarrowee East Moorabool River at Ballan 37:36 144:15 7 Apr. 95 + + + 6 Sep. 95 + + +

Yarrowee West Moorabool River at Gordon Road 37:37 144:01 7 Apr. 95 + + 6 Sep. 95 + + +

Yarrowee Yarrowee Creek at Arthurs Lane Bridge 37:49 143:55 6 Apr. 95 + + 7 Sep. 95 + +

Yarrowee Yarrowee Creek at Dowcra Street Bridge 37:36 143:51 6 Apr. 95 + + 7 Sep. 95 + + +

Yarrowee Yarrowee Creek at Durham Lead Bridge 37:43 143:51 6 Apr. 95 + + + 7 Sep. 95 + + +

Dee Don River u/s River Dee junction 24:06 150:08 21 Apr. 95 + + 26 Sep. 95 + +

Dee River Dee 1 km d/s Mt Morgan Mine 23:29 150:22 21 Apr. 95 + 25 Sep. 95 +

Dee River Dee 1 km u/s Mt Morgan Mine 23:39 150:25 20 Apr. 95 + + + 25 Sep. 95 + + +

Dee River Dee at Burnett Highway 23:45 150:21 21 Apr. 95 + + + 26 Sep. 95 + + +

Dee River Dee at Showgrounds Road 23:41 150:22 21 Apr. 95 + + + + 25 Sep. 95 + + +

Daylight Coolamigal Creek u/s Daylight Creek 33:12 149:58 17 Feb. 95 + + + + 1 Aug. 95 + + + +

Daylight Dark Corner Creek 33:18 150:56 16 Feb. 95 + + + 2 Aug. 95 + + +

Daylight Daylight Creek d/s Coolamigal Creek 33:12 149:58 17 Feb. 95 + + + 1 Aug. 95 + + +

Daylight Daylight Creek u/s Pinnacle Creek junction 33:15 149:48 17 Feb. 95 + + + 1 Aug. 95 + + +

Daylight Daylight Creek u/s Williwa Creek junction 33:17 150:57 16 Feb. 95 + + + 2 Aug. 95 + + +

Molonglo Monlonglo River at Bungendore Road 35:22 149:23 13 Feb. 95 + + + + 25 Jul. 95 + + +

Molonglo Molonglo River at Carwoola Plain 35:29 149:27 13 Feb. 95 + + + + 24 Jul. 95 + + +

Molonglo Molonglo River d/s Copper Creek junction 35:34 149:27 14 Feb. 95 + + + 24 Jul. 95 + + +

Molonglo Molonglo River u/s Captains Flat Reservoir 35:36 149:27 14 Feb. 95 + + + 24 Jul. 95 + + +

Molonglo Yandyguinula Creek 35:28 149:39 14 Feb. 95 + + + 25 Jul. 95 + + +

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