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Urban AUSRIVAS: An evaluation of the use of AUSRIVAS models for urban stream assessment.


CRC for Freshwater Ecology
P. Breen1,2, C. Walsh1,2, S. Nichols3, R. Norris3, L. Metzeling4, &. J. Gooderham2
1Melbourne Water Corporation, 2Water Studies Centre, Monash University, 3University of Canberra, 4Environment Protection Authority, Victoria

Contents

Foreword & Publication Details

Abstract & Acknowledgements

1. Introduction

2. Methods

3. Results and Discussion

4. Conclusions

5. References

Appendix 1

Appendix 2

Appendix 3

List of Figures & Tables

More About Ausrivas

AUSRIVAS web site

AUSRIVAS (Australian River Assessment System) is a prediction system used to assess the biological health of Australian rivers. AUSRIVAS was developed under the National River Health Program (NRHP) by the Federal Government in 1994, in response to growing concern in Australia for maintaining ecological values. The NRHP involves the major environmental agency in each state and territory and is centrally administered by Environment Australia (EA) and the Land and Water Resources Research and Development Corporation (LWRRDC). The AUSRIVAS predictive software was developed at the Co-operative Research Centre for Freshwater Ecology (CRCFE). (Added by B Hall for the Electronic Version).


Foreword

This report describes the outcomes of a research project conducted under the Urban Research and Development sub-program of the National River Health Program (NRHP).

The NRHP is an on-going national program established in 1993, managed by the Land and Water Resources Research and Development Corporation (LWRRDC) and Environment Australia. Its mission is to improve the management of Australia's rivers and floodplains for their long-term health and ecological sustainability, through the following goals:

  1. To monitor and assess the health of Australia's rivers.
  2. To enhance the management of river flows, water allocation and water use to ensure the sustainability of river and floodplain ecosystems.
  3. To encourage active management to improve the health of Australia's rivers, based on a sound understanding of the ecological and hydrological processes.
  4. To evaluate the effectiveness of river management actions at a national scale.

Urban streams and estuaries (i.e. those affected by runoff and discharges from urban areas) are an important subset of Australia's waterways. Most are degraded biologically, physically and chemically and therefore require appropriate methods to be developed for health assessment and management. The Urban R&D Sub-program, managed by the Water Services Association of Australia, comprises 8 research projects which were developed to meet research priorities for urban streams and estuaries within the goals of the NRHP and to complement existing NRHP projects on non-urban rivers. Thus, research focuses on development of standardised methods for assessing the ecological health of urban streams and estuaries which can be linked with data on water and sediment quality. The urban R&D projects commenced in 1996.

The definition of health in urban waterways used is "the ability to support and maintain a balanced, integrative, adaptive community of organisms having a species composition, diversity and functional organisation as comparable as practicable to that of natural habitats of the region".

The eight projects of the Urban Sub-Program are:
 
Decision support system for management of urban streams Dr John Anderson
Southern Cross University, Lismore
RIVPACS (River InVertebrate Prediction and Classification System) for urban streamsDr Peter Breen
CRC for Freshwater Ecology, Monash
University, Melbourne
DIPACS (Diatom Prediction and Classification System) for urban streamsDr Jacob John
Curtin University, Perth
Sediment chemistry- macroinvertebrate fauna relationships in urban streamsDr Nick O'Connor
Water EcoScience, Melbourne
Classification of estuariesDr Peter Saenger
Southern Cross University,Lismore
Literature review of ecological health assessment in estuariesMr David Deeley
Murdoch University, Perth
Estuarine health assessment using benthic macrofauna
 
Dr Gary Poore
Museum of Victoria, Melbourne
Estuarine eutrophication modelsDr John Parslow
CSIRO Marine Laboratories, Hobart

Publication Details

Published by:
Land and Water Resources Research and Development Corporation
GPO Box 2182 Canberra ACT 2601
Telephone: (02) 6257 3379
Facsimile: (02) 6257 3420
Email: public@Iwwrrdc.gov.au
WebSite: www.lwrrdc.gov.au
© LWRRDC

Published Electronically on au.riversinfo.org by the Environmental Information Association (Incorporated) with the permission of LWRRDC and Environment Australia. Environment Australia assisted by providing copies of the manuscript for electronic publication. The Natural Heritage Trust provided project funds which were used to assist in publishing this material. In the case of variation between this document and the hard copy original the original takes precedence. (Bryan Hall).

Disclaimer:
The information contained in this publication has been published by LWRRDC to assist public knowledge and discussion and to help improve the sustainable management of land, water and vegetation. Where technical information has been prepared by or contributed by authors external to the Corporation, readers should contact the author(s), and conduct their own enquiries, before making use of that information.

Publication data:
Urban AUSRIVAS: An evaluation of the use of AUSRIVAS models for urban stream assessment. CRC for Freshwater Ecology P. Breen1,2, C. Walsh1,2, S. Nichols3, R. Norris3, L. Metzeling4, &. J. Gooderham2 1Melbourne Water Corporation, 2Water Studies Centre, Monash University, 3University of Canberra, 4Environment Protection Authority, Victoria
Report No 5, LWRRDC Occasional Paper 12/99.

ISSN 1320-0992
ISBN 0 642 26766 9

Managing Agencies


Abstract

The feasibility of developing and using area specific urban AUSRIVAS models was evaluated using existing stream macroinvertebrate datasets for the Melbourne metropolitan and hinterland area. To obtain an adequate number of reference sites for model development a number of datasets had to be combined. Melbourne hinterland sites with predominantly rural landuse were adopted as reference sites for urban areas. Datasets from the CRC for Freshwater Ecology (lab-sorted samples) were combined with the Victorian EPA National River Health Program dataset and the Melbourne Water Corporation Streamwatch dataset (both field-sorted samples). It was determined that lab-sorted and field-sorted datasets had some quantitative differences in the taxa recorded for the same sites. However, despite these systematic differences, multivariate analyses of community composition indicated that the lab-sorted and field-sorted datasets contained similar information about the patterns in macroinvertebrate community composition.

Successful urban models were constructed using both family and lowest taxonomic level data for hinterland sites. O/E scores were negatively correlated to catchment imperviousness and biochemical oxygen demand. These variables are considered to be indicators of urban density and efficiency of pollutant delivery to streams. The experimental models suffered from a lack of appropriate reference sites and a poor physiographic coverage of reference sites. This is likely to be a problem for many large cities. Results from the urban family model and the EPA Victorian family model were not significantly different.

A potential alternative approach is presented which uses existing regional models to assess urban environments. This approach uses a model with minimally impacted reference sites and develops a relationship that accounts for the impact of urbanisation on the model output. Catchment imperviousness measures the degree of catchment landuse change and is generally un-related to the type or quality of drainage infrastructure. Consequently it can be used as a general measure of urbanisation and as a covariate against which other ecological indicators can be set. Deviation from this relationship can be used to identify sites that are more (or less) disturbed given a particular level of imperviousness.

Acknowledgements

The Water Services Association of Australia is acknowledged for project funding. The CRC for Freshwater Ecology, Melbourne Water Corporation and the Victorian Environment Protection Authority are thanked for access to their data. John Dean is thanked for making available data from the Yarra River. We also thank Dr Richard Marchant and Dr Phillip Suter for reviewing the report.


1. Introduction

The National River Health Program (NRHP) was formed in 1993 to assess the health of Australian rivers at the national scale. Under this program the Cooperative Research Centre for Freshwater Ecology developed the Australian River Assessment System (AUSRIVAS) to predict the health of rivers at the state and regional scale (Coysh et al. 2000). The current AUSRIVAS models were not specifically developed for use in urban areas. It was hypothesized that a model based on reference sites in the immediate region of Melbourne may provide greater sensitivity to urban disturbances than larger-scale regional models. This hypothesis was tested by comparison of the results of the model developed here with an existing Victorian model (Marchant et al., 1997). A major objective of the Urban R&D sub-program is to foster the development of stream health assessment techniques suitable for use at a smaller scale in urban areas.

AUSRIVAS is broadly based on the strategy of Wright et al. (1984). The procedure is summarised as follows. A series of minimally impaired sites are chosen as reference sites. Chessman (1995) describes a procedure for reference site selection. Reference sites are then classified into groups based on their macroinvertebrate community composition by cluster analysis. Environmental predictor variables that best discriminate among the groups of reference sites are then identified by such techniques as multiple discriminant function analysis. Environmental variables that are commonly influenced by human activity are usually excluded from the analysis. To use the model any number of test sites are then selected and, the limited set of environmental characteristics selected in the last step above are measured and the biota sampled. The environmental variables are used to match the test sites with groups of reference sites and test site probabilities of group membership are calculated. Once group membership of a test site has been determined from the environmental data, the taxa (at whatever taxonomic level has been used) that should occur at an individual site are predicted. Minimally impaired reference sites are selected to create the predictive models, thus the AUSRIVAS models predict the expected macroinvertebrate community structure at a test site independent of impact from anthropogenic factors such as changes in water quality or flow patterns. The difference between the observed community structure (O) at a test site and that expected (E) by the model can be used as an indicator of impact. Test sites equivalent to reference condition can be expected to have O/E taxa ratios close to one, whereas impacted sites can be expected to have O/E taxa ratios much less than one.

1.1 Project Objectives

The primary aim of the project was to develop a modeling approach for urban stream health assessment. Specific objectives included:

An important first step in the process was the task of constructing an adequate database to be able to undertake the modeling. A critical issue associated with this task was ensuring the compatibility of macroinvertebrate data collected using different processing techniques, eg. preserved laboratory sample sorting (lab-sorted) versus live sample sorting in the field (field-sorted).


2. Methods

2.1 Study Area and Site Selection

The study area was the Melbourne region of southeastern Australia. Reference sites were selected from the rural environment surrounding Melbourne. Test sites were within the Melbourne metropolitan area. Reference sites were in the Melbourne hinterland and were selected to represent typical to good quality rural landuse sites free from obvious sewage or stormwater discharges, dams, mining or forestry activities.

AUSRIVAS models operate best when the number of reference sites in the model is high (preferably above 30) and the reference site spatial distribution is similar to the distribution of the test sites. In order to compile a dataset with an adequate number of sites (within the study area, with similar habitat and sampling time) it was necessary to combine data from a number of organisations. Data for this project was pooled from a CRCFE study of urban stream ecology in the Melbourne region (Walsh et al., in press), Victorian EPA monitoring as part of the National River Health Program (L. Metzeling, unpublished data), and from Melbourne Water Corporation Streamwatch data collected by Water Ecoscience (Smith, Vertessy & Hardwick, 1997). The pooled dataset contained 31 reference sites and 31 test sites (Appendix 1).

The rationale behind using reference sites from surrounding rural areas lies in the proposition that rural streams, while subject to some anthropogenic disturbances, represent an achievable target for restoration of urban streams. The use of non-pristine sites as reference sites has been demonstrated in Britain (Wright, 1995). Such an approach assumes that the patterns of biotic community composition across the non-pristine reference sites remain primarily non-anthropogenic. The validity of this assumption is discussed in section 4.2.

It is further hypothesized that a model based on reference sites in the immediate region of Melbourne may provide greater sensitivity to urban disturbances that larger-scale regional models. This hypothesis will be tested by comparison of the results of the model developed here with an existing Victorian regional model (Marchant et al., 1997).

2.2 The Data

Because data for this study were derived from three institutions, the voucher collections of the EPA, Water EcoScience and the CRC FE at the Water Studies Centre were compared to eliminate cases of synonymy, or differences in identification, prior to analysis. The lowest level of taxonomic resolution used here was the lowest taxonomic level routinely identified by all three institutions (Appendix 2).

All samples were taken from 10m transects of riffle, using hand nets with 250 µm mesh (Rapid Bioassessment methods; Chessman, 1995). Samples taken by the CRC FE were preserved in the field and processed in the laboratory. Each sample was sub-sampled (Marchant, 1989) and sorted to remove 10%, or 300 organisms from each sample (Walsh, 1997). Samples taken by the EPA and the Streamwatch Program were sorted unpreserved in the field for 30 minutes.

Riffles were the only habitat sampled at enough reference sites for model construction. Samples were collected over three seasons: spring 1994 and 1995, and autumn 1995.

A primary requirement of combining data from the three institutions was to ensure compatibility of the two sample processing methods: field-sort and lab-sort. Therefore a subset of data was compiled consisting of sites that were sampled by each method within a period of two months (Appendix 3). The two samples (field-sort and lab-sort) from each site in each season is termed a _5method-pair_6. Two method-pairs exist for several sites, as samples were collected over two seasons. Lab-sort data for seven Yarra River samples and three Gardiners Creek samples were derived from the complete sorting of residues preserved after field-sorting. This data was reduced to the same level of processing used for all other samples using a computer program that simulates the Marchant subsampler (Walsh, 1997). Twenty-five pairs of riffle samples were used in the comparison.

Numbers of taxa recovered by the two methods were compared by a paired t test using un-transformed data. To evaluate any systematic taxonomic differences between the methods, the fidelity (Boesch, 1997) of each taxon to each of the methods was calculated with the following equation:

Inline Equation or Image

Where A is the number of samples in which the taxon occurs for one method; B is the total number of samples using that method; C is the total number of occurrences for both methods; and D is the total number of samples.

Patterns of community composition in the method-pair data were assessed using semi-strong hybrid multi-dimensional scaling (MDS) ordinations (Belbin, 1994) based on Bray-Curtis similarities for presence-absence data.

Three questions were explored in this assessment:

  1. Did the two methods result in the same patterns of community composition across the 25 samples?
  2. Were the differences in community composition within method-pairs small compared to the differences between samples?
  3. Can the differences between method-pairs be minimized by data manipulations such as a) the removal of taxa more commonly collected by one processing method, or b) the removal of rare taxa?

The removal of rare taxa (arbitrarily defined here as taxa occurring in less than 5 samples or 10% of the dataset) is a common manipulation that may be employed in multivariate analyses to simplify data sets and reduce background noise (Marchant et al,. 1997).

The above analyses were conducted for both lowest-taxonomic level and family level resolution. These preliminary analyses were used to decide the most appropriate data manipulation for the construction of models.

2.3 The Model

2.3.1 Classification Analysis

The first step in the model building process was to classify reference sites that had similar invertebrate compositions. This was done for two data sets, one based on lowest taxonomic level and the second at family taxonomic level using presence/absence data and PATN multivariate analysis package (Belbin 1994). The Bray-Curtis association measure was used on the recommendation of Faith et al. (1987) as a robust measure of association for cluster analysis and ordination. Groups were selected based on the dissimilarity level of the clusters, the tightness of the reference site clusters and how well agency staff thought the groups represented a _5type_6 of reference site. The agglomerative clustering technique, flexible Unweighted Pair-Group arithMetic Averaging (UPGMA) recommended by Belbin and McDonald (1993) was used to form the reference site groupings. The classifications were viewed as dendrograms allowing the fusion level, which divides sites into groups, to be selected.

2.3.2 Discriminant Function Analysis

The AUSRIVAS models use habitat features (predictor variables) from a site to predict which taxa should occur at that site in the absence of environmental stress. Habitat variables that may be affected by human activity should not be used as predictor variables. Variables such as turbidity, dissolved oxygen and phosphorus concentrations are often affected by human impacts and might provide spurious predictions if used to predict the membership of test sites to the reference site groups. In contrast, habitat features such as altitude, distance from source, latitude and longitude often make good predictor variables because they are rarely affected by human impacts.

The reference site groups from the UPGMA classification were entered as a variable into the reference habitat data set and a Stepwise Multiple Discriminant Function Analysis (MDFA) performed to select the predictor variables used in an AUSRIVAS model. The Stepwise MDFA was performed in the SAS statistical package version 6.12 (SAS Institute 1995). This procedure selected a subset of habitat variables that best discriminated among the groups of sites formed from the faunal classifications. The stepwise procedure included habitat variables one at a time, selecting at each step the variables that gave the best group discrimination. At each step of the analysis the significance of variables already included was checked and variables that were no longer significant were removed. The significance levels for variables to enter and be retained by the Stepwise MDFA both were set at 0.05.

The subsets of habitat variables from the Stepwise MDFA were then tested in a MDFA to predict the probabilities of group membership for a reference site. Biased discriminations were avoided by using the cross-validation option that predicts group membership of each site separately. A subset of habitat variables that produced the lowest error in predicting the group membership of reference sites was obtained from this procedure. However, the actual value of the misclassification error is not critical because AUSRIVAS uses all the probabilities of a site belonging to each group for site predictions, rather than the allocation to a single group as performed by the cross-validation procedure. Thus, sites with an affinity for two or more groups can be misclassified but still provide adequate predictions for a model.

The subsets of habitat variables obtained from the stepwise MDFA were used as predictor variables for the AUSRIVAS model under construction. The predictor variables and the reference site invertebrate-classification form the foundation of AUSRIVAS, allowing predictions of the taxa expected at new "test" sites. The list of predictor variables used in model construction is shown in Table 1.

2.3.3 Rare taxa

Rare (infrequently occurring) taxa are commonly removed from the invertebrate data sets. Rare taxa can have a high variance because of their sparse distribution and are often removed from multivariate analyses to reduce noise (Gauch 1982) and usually their exclusion has little effect on results (Smith et al. 1988). However, in this study the influence of data transformation was specifically evaluated. It was determined that rare taxa would be included (see 3.1.1).

2.3.4 Validating and defining the population of reference sites

The initial _5working_6 models were run in SAS because this was the primary tool used in developing the predictive rationale. The full reference invertebrate and habitat datasets minus sites deleted at the classification stage, were used to create the first version of the model. The same reference data set was then entered into the model as if they were test sites to validate reference site condition. Reference sites with observed to expected taxa ratios (O/E ratios) below 0.75 were investigated more closely and deleted from the model if found to be unsuitable as reference sites. The 0.75 cut off was based on the value judgement that a site missing 25% of the taxa expected to occur there was unlikely to represent reference conditions. The models were then reconstructed with the failed reference sites removed. The reduced reference site data sets were then run through the revised model. The O/E ratio output at this stage was considered to represent the distribution of ecological health for the population of reference sites.
 
Table 1. Description of predictor variables used in model construction
VariableDefinitionUnits
ALTITUDEAltitudem
AMGEASTAustralian map grid easting 
AMGNORTHAustralian map grid northing 
DEPTHMean water depth of rifflecm
DISCHARGEMean annual dischargeML
GRANITE% catchment area with Granitic surface geology%
RAINFALLMean annual rainfallmm
SEDALLOC% catchment area with "course" (gravel, sand) sedimentary alluvial surface geology%
SEDMARM% catchment area with Sedimentary marine (mudstone) surface geology%
SEDMARS% catchment area with Sedimentary marine (sandstone) surface geology%
SLOPESlope of site taken from map contours (1:50000)%

2.3.5 O/E taxa bands representing biological health

To simplify interpretation AUSRIVAS presents the O/E taxa ratios as bands that represent different levels of biological condition. The widths of the bands are based on the distribution of the O/E taxa values for the reference sites of each particular model. Test sites that fall between the 10th and 90th percentiles from the population of reference sites used to create the model are considered equivalent to reference condition (band A). Impaired test sites will fall into a band equivalent to the severity of impact that the site is experiencing. The next two bands (B and C), which represent increasing levels of impairment, are the same width as band A. Band D will vary in width depending on the range of the reference O/E taxa values. Test sites with biological condition richer than reference condition (i.e. > 90th percentile) are placed in band X.

2.3.6 Test site assessment evaluation

The objective evaluation of model test site assessments is a particular problem. To evaluate model output requires an independent site impact assessment. This study has developed an interim approach. In this study region, the work of Walsh et al. (in press) has identified a series of environmental variables as being correlated with the community structure of stream macroinvertebrate communities. The two major variables indicative of urban impact on macroinvertebrates were catchment imperviousness and the water quality variable BOD. Relationships between each of these variables and O/E were assessed. To assess their combined influence, these variables were combined into a composite variable (BODIMP).

BODIMP = Catchment imperviousness (proportion of catchment area) * BOD (mg.L-1)

The range of BODIMP is 0 (where BOD » 1 and impervious area = 0, ie. a typical undisturbed state) and 9.5 (where BOD = 10 and impervious area = 0.95, ie. polluted water with central business district level of urban development).

The range for AUSRIVAS index is 0-1, where 0 = no expected taxa observed and 1 = a match between the number of taxa expected (E) and the number of observed taxa (O). However, AUSRIVAS may produce an O/E taxa value greater than 1 when more than the expected number of taxa are observed in the sample.

This variable was calculated for a test dataset (Appendix 1) consisting of 16 lab-sorted and 15 field-sorted samples.


3. Results and Discussion

3.1 The Data

A quantitative comparison of sample processing methods indicated there were some systematic differences between lab-sorted and field-sorted samples. Using lowest taxonomic resolution, lab-sorted samples contained more taxa (mean 21.9) than field-sorted samples (mean 18.3: t = -3.823, P < 0.05). The numbers of families collected by the two methods were not different (lab-sorted mean 12.1, field-sorted mean 11.7: t = -1.113, P > 0.05).
 

Table 2. Fidelity analysis for twenty-five pairs of samples, each pair taken in the same season from the same site. One of each sample pair was field-sorted, and one was lab-sorted. Taxa that occurred more than twice as frequently in samples processed by one method than the other are listed. The analysis was conducted separately for two taxonomic levels

 

No. occurrences in

 

 

No. occurrences in

 

Lowest-taxon level

field-sort

lab-sort

Fidelity

Family level

field-sort

lab-sort

Fidelity

More common in lab-sort

Pisidium spp.

0

8

2.00

Sphaeriidae

0

8

2.00

Empididae SRV sp.1

0

6

2.00

Empididae

3

14

1.65

Austrolimnius sp.L25E

1

6

1.71

Ceratopogonidae

1

4

1.60

Nanocladius spp.

1

6

1.71

Nematoda

2

6

1.50

Cladotanytarsus spp.

2

10

1.67

Ancylidae

6

17

1.48

Austrolimnius group A spp.

1

5

1.67

Ecnomidae

3

7

1.40

Empididae SRV sp.6

1

5

1.67

 

 

 

 

Tanytarsus spp.

1

4

1.60

 

 

 

 

Oecetis spp.

1

4

1.60

 

 

 

 

Notriolus quadriplagiatus

2

7

1.56

 

 

 

 

Ecnomus continentalis

2

7

1.56

 

 

 

 

Nematoda

2

6

1.50

 

 

 

 

Cheumatopsyche sp.4

2

6

1.50

 

 

 

 

Ferrissia sp.

6

17

1.48

 

 

 

 

Botryocladius grapeth

6

15

1.43

 

 

 

 

Parakiefferiella spp.

3

7

1.40

 

 

 

 

Oligochaeta spp.

24

25

1.02

Oligochaeta

24

25

1.02

More common in field-sort

Parastacidae spp.

6

1

1.71

Parastacidae

6

1

1.71

Baetidae genus 1 spp.

5

1

1.67

Aeshnidae

8

2

1.60

Corbiculina spp.

6

2

1.50

Corbiculidae

6

2

1.50

Paratya australiensis

6

2

1.50

Atyidae

6

2

1.50

Simsonia longipes

5

2

1.43

 

 

 

 

Illiesoperla spp.

5

2

1.43

 

 

 

 


Lab-sorted samples more commonly included a number of small or immobile taxa than did field-sorted samples (Table 2). A smaller number of taxa were more commonly found in the field-sorted samples (Table 2), all of which were large or highly mobile taxa.

Despite these systematic differences, both processing methods produced similar patterns of community composition, (Fig. 1). Samples fell into four groups:

  1. Yarra river sites (Y1 in spring 1994, and Y5, Y6 and Y7 in spring 1994 and autumn 1995);
  2. Eastern hinterland small stream sites (D1, O2 and W5 in spring 1994 and autumn 1995);
  3. Western hinterland small stream sites (E5 and J3 in spring 1994 and autumn 1995);
  4. Urban small stream sites, all within the Melbourne metropolitan area (G3, G4, G5 and U5 in spring 1994, and M5 and D5 in spring 1994 and autumn 1995).

The major difference between patterns for the two methods was the greater dissimilarity within the western hinterland communities of in field-sorted samples than in lab-sorted samples (Fig 1).
Figure 1. MDS ordinations
Inline Equation or Image
(original size image)
Figure 1. MDS ordinations of 25 sites used in the comparison of processing methods, based on lowest taxonomic level resolution, using all taxa. (a) Field sorted samples, (b) lab-sorted samples. Four groups of sites (eastern hinterland small streams, western hinterland small streams, Yarra River, and metropolitan sites) are grouped by kernel density contour curves, P=0.9.

The four groups of samples remained evident when method-pairs were included in the ordination (i.e. two sets of data were included for each sample: one field-sorted, one lab-sorted. Figs. 2a, 3a). For lowest-taxonomic level resolution, most differences between method-pairs were small compared to differences between sample groups (Fig 2a). For family-level resolution, the same was true for the hinterland small stream groups, but systematic differences between method pairs were evident, particularly in the urban group, but also in the Yarra group (Fig. 3a).

Biased taxa were arbitrarily defined as those that were ³ 3 times as common in one processing method than the other (Fidelity ³ 1.5, Table 2). In addition, Oligochaeta were present in all lab-sorted samples, but absent from one field-sorted sample. In light of the ubiquity of oligochaetes, and the increased chance of their omission from field-sorted samples, these were also included as biased taxa. Removal of biased taxa prior to calculating ordinations tended to reduce method-pair differences within the urban group, but the effect on method-pair differences in the other groups was small (Fig. 2b, 3b). However, the removal of biased taxa tended to reduce differences between hinterland groups, particularly at the family-level.

Metropolitan sites support depauperate macroinvertebrate communities that showed little pattern that was explicable by environmental variation (Walsh et al., in press). In the absence of strong patterns associated with environmental variation, it was not surprising that method-derived bias had a strong influence on patterns within urban sites. The lesser effect of biased taxa for hinterland sites at the lowest taxonomic level of resolution suggests that patterns associated with environmental variation among these sites were strong compared to patterns associated with method bias. The relatively greater effect of biased taxa at the family level may be due to the fewer number of taxonomic groups in the analysis and that some of the biased families contributed to group separation.

At the lowest-taxonomic level, the removal of rare taxa reduced some method-pair differences but also increased others. The removal of rare taxa had little effect on the differences between sample groups (Fig. 2c). The removal of rare families from the family-level ordination both increased and decreased method-pair differences, but more importantly, obscured differences between sample groups (Fig. 3c).

Removal of both rare and biased taxa did not improve ordination patterns at either taxonomic level (Figs. 2d, 3d).

3.1.1 Model construction using mixed-method data

Because the inclusion of all taxa, either families or species, resulted in the best group separation in community composition analyses, the models were based on all taxa. Removal of rare and biased taxa tended to obscure community patterns, particularly at the species level.
Figure 2. MDS ordinations
Inline Equation or Image
(original size image)
Figure 2. MDS ordinations of 25 sites used in the comparison of processing methods based on lowest taxonomic level resolution. Lines join method-pairs: i.e. samples taken from the same site in the same season but processed by different methods (field-sort and lab-sort). Large symbols indicate field-pick samples. Grouping conventions as in Figure 1. (a) Using all taxa, (b) biased taxa (with fidelity to one method of ³ 1.50, and Oligochaeta) removed, (c) rare taxa (occurring in <10% of samples) removed, (d) biased and rare taxa removed.


Figure 3. MDS ordinations
Inline Equation or Image
(original size image)
Figure 3. MDS ordinations of 25 sites used in the comparison of processing methods based on family-level resolution. Lines join method-pairs: i.e. samples taken from the same site in the same season but processed by different methods (field-sort and lab-sort). Large symbols indicate field-pick samples. Grouping conventions as in Figure 1. (a) Using all families, (b) biased families (with fidelity to one method of ³ 1.50, and Oligochaeta) removed, (c) rare families (occurring in <10% of samples) removed, (d) biased and rare families removed.

3.2 The Model

The reference site groupings (Table 3) produced in the classification analysis of the biological data reflect some clearly recognisable physiographic and geomorphologic characteristics of the sites (Table 4). The descriptions of the reference site groups reflect the predictor variables chosen in the models (Table 5). This may be expected and acts to increase user confidence in the model, however it is not necessarily a characteristic of a successful model.
 

Table 3. Reference site group membership

Family Model

Lowest Taxon Model

Site CodeSiteGroupSite CodeSiteGroup
BI111BI111
BI321BI322
BI431BI432
BI541BI542
CA151CA153
CT161CT163
DA172DA171
DP283DP284
DP393DP394
DP4103DP4104
DP5113DP5114
JA1123JA1124
JA2133JA2134
JA3143JA3144
JA5153JA5154
JA6163JA6164
LA2173LA2173
LI1181LI1183
MK1193MK1194
OL1202OL1201
OL2212OL2211
OL3221OL3223
PL1231PL1232
SH1242SH124Excluded
WC2253WC2253
WO1262WO1261
WO2272WO2271
WO3282WO3281
WO4292WO4291
WO5302WO5301
WO6311WO631Excluded


 

 

Table 4. General physiographic description of model groups

Family Model

Lowest Taxon Model

Group 1Low slope streams on granitic geology with homogeneous sandy substratum and moderate rainfallGroup 1Eastern ranges high slope streams on granitic geology with heterogeneous gravel substratum and moderate rainfall
Group 2Eastern ranges high slope streams on granitic geology with heterogeneous gravel substratum and moderate rainfallGroup 2Low slope streams on granitic geology with homogeneous sandy substratum and moderate rainfall
Group 3Western plains, low rainfall, low slope streams on basalt geology with gravel/cobble substratumGroup 3Moderate slope deep water streams on mixed, but predominantly granitic geology, with heterogeneous gravel substratum and moderate rainfall
Group 4Western plains, low rainfall, low slope streams on basalt geology with gravel/cobble substratum

The list of predictor variables and the misclassification errors for both the Family and Species models are provided in Table 5.
 

Table 5. Predictor variables used in particular models

Model

Error

Predictor Variables

Family0.20AMGeastAMGnorthGraniteRainfallSedmarmSedmarsSlope
Species0.28AltitudeAMGeastBasaltDepthDischargeSedallc 

When setting the O/E bands only O/E scores between the 10th and 90th percentile from the population of reference sites used to create the model are considered as equivalent to reference condition (Band A). Therefore, assuming the validation reference sites are representative of the reference site population then up to 20% may be expected to fail or be misclassified. The bands used for the urban models are shown in Table 6.
 
Table 6. Bands representing different levels of biological condition for the urban models
 

O/E Bands

Model

XABCD

Family

>1.150.85-1.150.54-0.840.23-0.530.00-0.022

Species

>1.220.78-1.220.33-0.770.00-0.32No band D

Family model group membership displays some inconsistencies when explaining the groups in terms of the predictor variables, for example PL1 in group 1, SH1 in group 2, and LA2 and WC2 in group 3. PL1 has high slope and the lowest rainfall of the group and at least half the catchment is on marine sedimentary sandstones. SH1 is predominately on marine sedimentary sandstones. At least half the catchment of LA2 is on coarse sedimentary alluvium. This latter geological group is very poorly represented in the reference sites and LA2 is the only site with a high proportion of this geology in its catchment. WC2 is predominately on marine sedimentary sandstones. This suggests that for at least some sites group membership may be determined simply by geographic proximity. SH1, LA2 and WC2 are also all field-sorted sites. The introduction of a fourth group in the species model resolves many of these physical inconsistencies. However, while the inclusion of an extra group in the species model (Group 3) provides more consistent physiographic groups it also further reduces the number of sites in the groups. Both Groups 2 and 3 in the species model are numerically under represented in the reference dataset and models. Also while the introduction of Group 3 may be physiographically accurate, five out of the six sites in the group are field-sorted sites. Even though the lab-sort and fields-sort data have been shown to contain similar community patterns (Figure 1) this kind of field-sorted grouping raises some concerns about how systematic differences in the data may be influencing the models.

3.2.1 Test site evaluation

Walsh et al. (in press) found that median BOD (14 monthly samples)(hypothesised to be an indicator of the efficiency of pollutant delivery by stormwater drainage systems) and catchment imperviousness explained patterns in macroinvertebrate community composition between metropolitan and hinterland sites in Melbourne. These variables and the combined variable BODIMP are used to objectively evaluate urban model ratings of test sites. The relationship between O/E ratios and BOD and catchment imperviousness were non-linear (Figure 4). Three groups of sites account for a large proportion of the scatter in these relationships (Figure 4b).

This emphasizes the multifaceted nature of urban impact. By combining BOD and catchment imperviousness into a composite urban impact variable (BODIMP) the influence of these out lying groups is reduced and the relationship is more linear. The Pearson correlation coefficients for the O/E _0 BODIMP relationship for the lowest taxon and family models are _0 0.555 and _00.562 respectively.

A third group of sites is also evident.

These sites have higher O/E ratios than expected given the degree of urban development in their catchment. A potentially significant feature of these sites is that they all have physically stable rocky benthic habitat and relatively intact native riparian habitat and suggests the impact of urbanisation does not necessarily follow a uniform pattern.

The mean rank difference between BODIMP and urban model assessments was high (>6), suggesting some substantial differences between the assessments (Table 7) but the overall ordering of the sites was good given our understanding of the sites (Figure 4). The sites in good condition were clearly identified (CT2, DP6, JA4, MA2) by both models and the BODIMP variable. Agreement on the assessment of intermediate sites was good. Most discrepancy appears to occur among the most impacted sites. For instance, sites in both Dandenong (DA2, DA3, DA5) and Gardiners (GA3, GA4, GA6) Creeks had variable outputs from the lowest taxon and family models relative to BODIMP. There is some suggestion that benthic stream communities in highly urbanised catchments like Dandenong and Gardiners Creeks are in a constant state of recovery from a whole range of different impacts (including some not incorporated in the BODIMP variable). For example MK3 and MK4 have medium BOD and low catchment imperviousness and appear to be more severely impacted than the simple BODIMP variable would suggest. Contradictions such as this provide useful opportunities for identifying specific urban impacts beyond those expected from a particular level of development. However while the models and the independent assessment variable (BODIMP) showed some considerable variation in detail, the overall broad evaluations of the relative impact or condition of the test sites was similar
 
Table 7. Test site assessment by urban models and an objective urban impact variable (BODIMP)
SiteUrban _0 fam. O/EUrban _0 fam.BandUrban _0 fam.RankUrban _0 spp O/EUrban - spp BandUrban - spp RankBODIMPInverse BODIMP RankRank differences

fam. & spp.

BODIMP & fam.

BODIMP & spp.

BC1*0.42C150.38B20 1.1192151419
CT2*0.86A280.92A310.029427314
DA20.17D30.19C80.85175523
DA30.25C60.12C20.74277415
DA50.42C160.12C30.711881385
DB3*0.47C200.36B190.338215154
DB4*0.17D40.12C10.29981631215
DP6*1.12A310.84A300.003231101
EC2*0.22D50.21C90.18331941410
GA30.33C110.17C50.856651
GA40.17D10.24C120.95211110
GA6*0.17D20.14C40.86034220
JA4*0.9A290.81A290.009329000
KO2*0.26C70.18C60.13142111415
MA2*0.9A300.69B280.007230202
MK20.6B250.42B250.016128033
MK30.3C100.21C100.0612401414
MK40.26C80.18C70.07682211415
MK5*0.34C120.21C110.269717156
MK60.3C90.27C130.346914451
MU30.38C130.42B240.59281111213
MU4*0.55B230.27C140.5859129112
MU50.5C210.32C160.8626351813
MU60.55B220.38B210.7022911312
MU70.45C190.44B 260.5611137613
OL50.42C170.4B230.139920633
PL60.56B240.38B220.036426224
PL70.43C180.34B180.066623055
SL1*0.39C140.3C150.62310145
TY1*0.69B270.33C170.216181091
WA1*0.66B260.62B270.059725112
            
        Mean3.876.266.65
(* Indicate field-sort site)SD3.695.435.60

 
Figure 4. Scatter plots
Figure 4
(original size image)
Figure 4. Scatter plots showing the relationship between urban model O/E ratios and BOD, Catchment Imperviousness and BODIMP. Enclosed groups of sites are discussed in text

 
Table 8. Summary of Wilcoxon test results comparing urban model outputs against an objective urban impact variable
  BODIMP (1) vs Family O/E (2)

BODIMP (1) vs Species O/E (2)

Family O/E (1) vs Species O/E (2)
Count of differences   
1 >2192527
2>11264
Z-0.852-3.567-4.184
Probability0.394<0.005<0.005

Results of Wilcoxon paired sample test comparisons between the model assessments and site condition evaluation using the (inverse) BODIMP variable are shown in Table 8. The results of the Wilcoxon test indicate there was a significant difference between the means of the BODIMP variable and species O/E ratio, and the mean O/E ratios of the family and species models. Species model O/E ratios were generally lower than the family O/E ratios as indicated by the count differences in Table 8.

The method pair dataset (Appendix 3) was used to compare the Urban models to the Victorian family model (Table 9). There was no significant difference between the O/E ratios of the family models (Wilcoxon test p = 0.099) and the relative O/E ratios were highly correlated (Pearson correlation coefficient = 0.943). The O/E ratios from the urban family model and the Victorian family model were both reasonably well correlated with BODIMP with Pearson correlation coefficients of _00.708 and _00.574 respectively. Marchant et al. (1997) found similar correlation between O/E ratios for sites in the Yarra catchment and a range of long-term water quality variables. Part of the rationale for using hinterland sites, with rural landuse, as reference in the urban models was the expectation that metropolitan sites would be severely downgraded and poorly resolved in the regional models. From the data available for this study it appears this is not the case.

The similar performance of the Urban and EPA Victorian models suggests broader regional AUSRIVAS models adequately assess urban impacts in metropolitan areas. The use of the broader regional models would avoid the considerable problem of selecting reference sites for urban streams.

A basic assumption of the reference site approach is that they should have minimal impact from human activity. This allows patterns in community structure to be well explained by relatively simple (physical) variables un-related to anthropogenic influences. There are two major difficulties with using genuine reference sites for urban streams. Firstly in most large cities whole physiographic regions can be developed leaving no local reference sites for certain terrain types. Secondly where genuine reference sites are available they set an unreasonably high target. Urban development by its very nature is going to change the physical characteristics of a catchment. This means that no matter what remedial management actions were undertaken, urban sites could never approach genuine reference site condition.
 
Table 9. Comparison of Urban models and the EPA Victorian family model
Site Data Type Urban _0 fam O/E Urban - fam Band Urban _0 spp O/E Urban - spp Band VIC _0 fam O/E VIC - fam Band
DA1 Lab. 1.03 A 1.04 A 0.72 B
  Field 0.65 B 0.63 B 0.77 B
DA5 Lab. 0.41 C - - 0.25 C
  Field 0.25 C - - 0.20 C
DP5 Lab. 0.95 A 1.05 A 0.80 B
  Field 1.08 A 0.84 A 1.07 A
GA3 Lab. 0.33 C 0.17 C 0.22 C
  Field 0.33 C 0.18 C 0.30 C
GA4 Lab. 0.17 D 0.24 C 0.12 C
  Field 0.22 D 0.15 C 0.12 C
  Field 0.26 C 0.19 C 0.18 C
GA7 Lab. 0.34 C - - 0.26 C
  Field 0.22 D - - 0.35 C
JA3 Lab. 0.86 A 0.87 A 0.88 A
  Field 0.86 A 0.81 A 0.96 A
MK5 Lab. 0.30 C 0.27 C 0.15 C
  Field 0.17 D 0.12 C 0.15 C
OL2 Lab. 0.80 B 1.11 A 0.66 B
  Field 0.79 B 1.01 A 0.66 B
WO5 Lab. 0.89 A 1.10 A 0.90 A
  Field 0.97 A 1.05 A 1.03 A
Y5 Lab. 0.82 B - - 0.82 B
  Field 0.56 B - - 0.65 B
Y6 Lab. 0.7 B - - 0.61 B
  Field 0.7 B - - 0.74 B
Y7 Lab. 0.63 B - - 0.65 B
  Field 0.63 B - - 0.73 B

Given this understanding our initial response to this problem was to choose reference sites that were perceived as a reasonable target for the urban environment. In general these were hinterland sites of the Melbourne metropolitan area that were predominately rural landuse but inevitably also contained some satellite urban development. In a practical sense this approach worked; satisfactory models were constructed. However, the reference sites were clearly influenced by urbanisation which is an impact the models are intended to evaluate. Walsh et al. (in press) demonstrated that catchment imperviousness, a measure of urban density, was a dominant correlate explaining patterns of macroinvertebrate community composition among hinterland sites to the east of Melbourne. Thus the patterns of community composition among many of the sites used as reference in this study could be well explained by urbanisation, the same impact the models were being constructed to detect.

An alternative is to use a model with minimally impacted reference sites and to develop a relationship that accounts for urbanisation. Catchment imperviousness measures the degree of catchment landuse change and is generally unrelated to the type or quality of drainage infrastructure. Consequently it can be used as a general measure of urbanisation that can be used as a covariate against which other ecological indicators can be set (Walsh et al. .in press). O/E ratios from the EPA Victorian family model were negatively correlated to catchment imperviousness of both reference and test sites from this study (Figure 5). However, a number of sites had both low O/E ratios and low catchment imperviousness, suggesting impacts in addition to that explained by imperviousness alone.

Among hinterland sites, where stormwater drainage is largely through open earthen drains or small localized pipes, a decline in O/E is evident up to a maximum imperviousness of 0.12. Three eastern hinterland sites (Cockatoo 1, Woori Yallock 3 and Olinda 2) have low O/E scores, which may be a result of impacts not directly associated with catchment imperviousness.

O/E is low for almost all metropolitan sites (where stormwater is intensively drained through pipe networks). The western metropolitan site with the relatively high O/E score is Taylors Ck. Most urban areas of this catchment drain into a series of lakes which act as stormwater treatment ponds. The eastern metropolitan sites with highly impervious catchments and relatively high O/E scores are Ruffey Ck and sites on Mullum Mullum Ck. Ruffey Ck has a stormwater flood retarding basin and stormwater treatment wetland in its upper catchment. Mullum Mullum Ck is characterized by moderately well preserved riparian zones and good rocky instream habitat that is resistent to the increased flows experienced in urban streams.

The observed spread of O/E ratios around the negative relationship with catchment imperviousness creates an opportunity to band O/E ratios weighted by catchment imperviousness. This has been done in a subjective way in Figure 5 where a series of bands have been hand fitted. The slope of the bands allows for the handicap of catchment imperviousness. The spread of O/E ratios around the general catchment imperviousness relationship represents variation in stream community condition not explained by catchment imperviousness. Factors explaining this spread include water quality and the nature of the drainage infrastructure (Walsh in press, Walsh et al. in press) and water quality treatment ponds and wetlands (see Ruffey Creek in Figure 5). The band interval can be used as a measure of the degree of variation between the observed condition (O/E ratio) and that expected given a particular level of catchment imperviousness. The best existing condition in Melbourne streams is represented by band A, and bands B and C represent progressively poorer condition. The band A+ is suggested as a hypothetical target for catchments where stormwater best management practice has been adopted. However, there has been virtually no assessment of the efficacy of catchment scale stormwater best management practice on the health of receiving waters.

A systematic means for determining band limits needs to be formalized. One approach would be to develop a dataset with an even number of sites across the catchment imperviousness range and a spread of stream community conditions (O/E ratios) within any narrow range of catchment imperviousness. The number of sites would need to be high enough so that within a narrow range of catchment imperviousness O/E percentiles could be calculated. Regression lines could be developed for various percentiles as a method of establishing bands. The regression relationships would not need to linear. This approach remains a topic for future research.

The use of regional AUSRIVAS model O/E ratio versus catchment imperviousness relationships is a potential alternative to specific urban models. This relationship provides an informative use of the Victorian AUSRIVAS model for managers of catchments containing any urban land use. It provides targets for better stormwater practices based on best available information. However, no catchments have yet been studied that have been developed using contemporary best management practices. Scores within the A+ band may be targets for such catchments where Water Sensitive Urban Design principles have been applied.
 
Figure 5. Relationship between O/E ratios
Figure 5
Figure 5. Relationship between O/E ratios from the EPA Victorian family model and catchment imperviousness for a selection of reference and test sites. Preliminary banding is shown:

A+ Better than expected/Potential target for stormwater best management practices
A Best existing condition
B Poor condition
C Very poor condition



4. Conclusions

4.1 The Data

Laboratory and live-pick field sorted sample processing result in quantitative and systematic differences in the taxonomic composition of datasets. Lab-sorted samples typically contained more smaller and immobile taxa, whereas field-sorted samples contained more large and highly mobile taxa. However, separate analysis of community of composition showed lab-sorted and field-sorted samples contained similar community patterns and resulted in similar site groupings in MDS ordinations. Ordination analysis of the combined method-pair dataset indicates that differences between community groups were greater than differences between the method pairs. Data manipulations to remove rare taxa, method biased taxa or both, did little to increase the coupling between method pairs, and generally resulted in obscuring the differences between groups.

Although particular differences were evident in the community data analyses, the broad patterns in datasets were similar. It was considered the datasets could be combined and used for evaluating the potential for urban AUSRIVAS modeling. However the differences would suggest that data sets of this type should not be routinely combined and that the differences between the datasets may have caused some of the variation in O/E scores for samples from the same site collected by different methods.

4.2 The Models

The reference site groupings produced in the classification analyses for model construction reflect some clearly recognisable physiographic and geomorphologic characteristics of the study area. However, it is clear some regions are under-represented in the reference site dataset. For example Groups 2 and 3 in the lowest taxon model (Table 4). In spite of the small size of the reference site dataset misclassification errors for the models were satisfactory.

Both the family and lowest taxon urban models produced O/E scores that were negatively correlated to BOD and catchment imperviousness. While there were some ranking difference between the assessments the overall evaluation of sites were similar and generally reflected impact from identified disturbance factors in the study area (Walsh et al. in press). O/E scores from the urban family model and the Victorian family model (Marchant et al. 1997) were not significantly different in rank-order, and their ranges were similar. Thus the urban models developed here were not found to be any more sensitive to urban impacts than a larger-scale model.

Arising out of this study and the work of Walsh et al. (in press) is a potential alternative to the development of area specific urban models. This involves the development of a general relationship between community composition and catchment imperviousness. The assumption is that even with the best management practices used in urban development, once catchment imperviousness increases above a certain level some community impact will be inevitable. Catchment imperviousness is used as a general measure of urban development. Community composition could be assessed using various approaches but in this case O/E ratios from the EPA Victorian model were used. Test sites would be assessed using established regional AUSRIVAS models and the resulting O/E ratios evaluated against an O/E _0 catchment imperviousness relationship. In this sense catchment imperviousness is used as a "handicapping" variable for the O/E ratios. A major attraction of this approach is that it avoids the problem of selection and availability of reference sites to construct urban specific models for assessment of urban streams.

The development of urban AUSRIVAS models is clearly possible and the models can be useful in discriminating between the relative condition of sites within metropolitan areas. However the present study suggests that area specific models (eg. urban), as developed here, need to be built on a larger more representative group of reference sites. The number of reference sites (31) in the present study appears to be too small to adequately cover the geographic range of the Melbourne metropolitan area. This study was restricted to using existing data but with a more strategic approach to sampling and reference site selection improved models may be possible. However, the problem of patterns of community composition among hinterland sites being strongly correlated with the degree of catchment urbanisation is likely to be common to large, sprawling cities such as Melbourne. Such relationships compromise the appropriateness of using hinterland sites as reference sites for models designed to assess urban effects.


5. References


Appendix 1 Reference and test sites used to create the AUSRIVAS Urban Family and Lowest taxon predictive models.


Site Code

Data

Source

Stream

Location

Site

Status

AMG

east

AMG

north

Catch.

Area

(Km2)

Altitude

(m2)

BI1CRC FEBunyip RiverBack Creek (Dyers Picnic Ground)R38355799034.96130
BI3CRC FEBunyip RiverLabertouche RdR390757951140.172
BI4CRC FEBunyip RiverBunyip SyphonR390757897277.9158
BI5CRC FEBunyip RiverPrinces Hwy above TarragoR390057833392.4743
CA1MWCardinia CreekBoundary/Manestar Rd (Harkaway)R35775795637.997
CT1EPACockatoo Creekd/s Brisbane Rd bridge CockatooR3685579819.7200
DA1CRC FEDandenong CreekDoongala Forest (Dandenong Ranges NP)R3543580971.63260
DP2CRC FEDeep CreekGallaghers Ford (Romsey)R305358626294.57380
DP3CRC FEDeep CreekDarraweit GuimR314258578490.62233
DP4CRC FEDeep CreekKinnears Rd (Bulla)R310558438637.59154
DP5CRC FEDeep CreekSunbury Rd (Bulla)R305858327880.3287
JA1CRC FEJacksons CreekPark in GisborneR286858485104.25410
JA2CRC FEJacksons CreekSettlement Rd (Clarkfield)R300158483292.76230
JA3CRC FEJacksons CreekVaughan St (Sunbury)R300458383345.32186
JA5CRC FEJacksons CreekBulla-Diggers Rest Rd (Bulla)R303958335372.75120
JA6CRC FEJacksons CreekOrgan Pipes National ParkR302858290395.0957

LA2

MW

Lang Lang River

Drouin/Poowong Rd (Athlone)

R

3936

57673

277.9

75

LI1EPALittle Yarra RiverLowe Rd u/s of Yarra tunnelR378358177147.1110
MK1CRC FEMerri CreekSummerhill Rd (Cragieburn)R320258395203.14192
OL1CRC FEOlinda CreekFalls Rd (Mt Dandenong)R3562581092.73412
OL2CRC FEOlinda CreekMt Evelyn Recreation CampR35775815522155
OL3EPAOlinda CreekYork Rd Mt EvelynR35695815423130
PL1CRC FEPlenty RiverAbove TourrorrongR33695851715.07240
SH1MWShepherd CreekBeenak Rd, GembrookR37385803516.6176
WC2EPAWatsons CreekHenley Rd Kangaroo GroundR34695825682.850
WO1CRC FEWoori Yallock CreekBeagley_6s Picnic Ground (Olinda)R3572580656.56308
WO2CRC FEWoori Yallock CreekMoxham_6s Rd (Monbulk)R35995805813.27198
WO3CRC FEWoori Yallock CreekOld Emerald Rd (Monbulk)R36265804721.78160
WO4CRC FEWoori Yallock CreekSwales Rd (Macclesfield)R36455808852.57132
WO5CRC FEWoori Yallock CreekMacclesfield-WY Rd (Yellingbo)R36825813683.01107
WO6EPAWoori Yallock Creekd/s Parslows bridge (Yellingbo)R368458140275.91105
BC1EPABrushy CreekHomestead Rd Wonga ParkT34935822822.655
CT2MWCockatoo CreekTschampions Rd, NanganaT37045806848.8130
DA2CRC FEDandenong CreekKing St (Bayswater)T34735810831.795
DA3CRC FEDandenong Creeku/s Boronia Rd (Vermont)T34265809880.2575
DA5CRC FEDandenong CreekJells Park (Wheelers Hill)T341558042139.9353
DB3EPADarebin CreekChenies Rd KingsburyT32635824291.270
DB4EPADarebin CreekAbercorn Av FairfieldT326658176114.340
DP6MWDeep CreekTrap St, BullaT305758326880.6285
EC2MWEummemmering CreekProgress Rd, DandenongT34495792249.619
GA3CRC FEGardiners CreekStation St (Box Hill)T33445810229.457
GA4CRC FEGardiners Creekd/s Warrigal Rd (Chadstone)T33205806542.8735
GA6MWGardiners CreekPearce St. Glen Iris (Eric Raven Reserve)T32945807777.6124
JA4MWJacksons CreekSunbury Rd, SunburyT300558378347.53189
KO2MWKororoit CreekWarmington Rd, SunshineT309058141242.120
MA2MWMaribyrnong RiverCalder Fwy, KeilorT3096582301314.2521
MK2CRC FEMerri CreekBarry St (Lalor)T321258290291.06104
MK3CRC FEMerri CreekMahoneys Rd (Campbellfield)T321258266304.986
MK4CRC FEMerri CreekKeady St (Coburg North)T320558229311.2550
MK5EPAMerri CreekCole Cres CoburgT322058198387.4240
MK6CRC FEMerri CreekHeidelberg Rd (Clifton Hill)T324258162411.7220
MU3CRC FEMullum Mullum CreekDeep Creek Rd (Mitcham)T3425581369.6987
MU4EPAMullum Mullum CreekQuarry Rd (Mitcham)T3418581421180
MU5CRC FEMullum Mullum CreekPark Rd (Donvale)T34135816020.1860
MU6CRC FEMullum Mullum CreekTindalls Rd (Donvale)T34055817724.3852
MU7CRC FEMullum Mullum CreekWarrandyte Rd (Doncaster East)T33925820134.0934
OL5CRC FEOlinda CreekGardiner St (Lilydale)T35455820357.9897
PL6CRC FEPlenty RiverPara Rd (Greensborough)T333358247353.8533
PL7CRC FEPlenty RiverRosanna Golf Club, just u/s of YarraT332358203366.7915
SL1EPASteeles CreekKeilor Rd at fordT31255821819.340
TY1EPATaylors CreekBurrowye Cres KeilorT30725824016.360
WA1MWWandin Yallock CreekSunnyside Rd, Seville EastT36695820218.7130


Appendix 2 The taxa used in the construction of the urban models.


EPA TaxoncodeFamilyLowest common taxonomic level
II999999NematodaNematoda (Unident.)
KG029999HydrobiidaeHydrobiidae (Unident.)
KG060199AncylidaeFerrissia sp.(Unident.)
KG9999A1PhysidaePhysa/Physastra sp.
KP020199CorbiculidaeCorbicula sp.(Unident.)
KP030199SphaeriidaePisidium sp.(Unident.)
LO999999OligochaetaOligochaeta (Unident.)
MM999999HydracarinaMites (Unident.)
OP020199CeinidaeAustrochiltonia sp.(Unident.)
OP030101EusiridaePseudomoera gabrieli
OP060104ParamelitidaeAustrogammarus haasei
OT010101AtyidaeParatya australiensis
OV019999ParastacidaeParastacidae (Unident.)
QC209999ScirtidaeScirtidae sp.(Unident.)
QC340199ElmidaeAustrolimnius sp.(Unident.)
QC3401B1ElmidaeAustrolimnius sp.L25E
QC3401B2ElmidaeAustrolimnius sp.L58E
QC3401B4ElmidaeAustrolimnius group A spp.(Larva)
QC3401C2ElmidaeAustrolimnius sp.L10E
QC34020HElmidaeSimsonia longipes (Larva)
QC340299ElmidaeSimsonia sp.(Unident.)
QC3402A9ElmidaeSimsonia sp.L12E
QC3402B1ElmidaeSimsonia sp.L2E
QC34031FElmidaeNotriolus victoriae (Larva)
QC34031ZElmidaeNotriolus quadriplagiatus (Larva)
QC340399ElmidaeNotriolus sp.(Unident.)
QC340499ElmidaeKingolus sp.(Unident.)
QC370102PsephenidaeSclerocyphon fuscus
QC370103PsephenidaeSclerocyphon maculatus
QC370104PsephenidaeSclerocyphon striatus
QC399999PtilodactylidaePtilodactylidae (Unident.)
QD0199A1TipulidaeTipulidae SRV sp.4
QD0199A2TipulidaeTipulidae SRV sp.5
QD0199A4TipulidaeTipulidae SRV sp.18
QD0199B2TipulidaeTipulidae EPA sp.1
QD0199B5TipulidaeTipulidae EPA sp.4
QD0199B9TipulidaeTipulidae EPA sp.8
QD0901A1CeratopogonidaeBezzia SRV sp.3
QD0901A3CeratopogonidaeCeratopogonidae EPA sp.3/15
QD0999B9CeratopogonidaeCeratopogonidae SRV sp.4
QD100102SimuliidaeAustrosimulium furiosum
QD100103SimuliidaeAustrosimulium montanum
QD100202SimuliidaeSimulium ornatipes
QD229999AthericidaeAthericidae (Unident.)
QD3599A1EmpididaeEmpididae SRV sp.1
QD3599C1EmpididaeEmpididae NMV sp.3
QD3599C4EmpididaeEmpididae NMV sp.2
QDAD0499Chironomidae, Subfam. PodonominaePodonomopsis sp.(Unident.)
QDAE0599Chironomidae, Subfam. TanypodinaeApsectrotanypus sp.(Unident.)
QDAE0899Chironomidae, Subfam. TanypodinaeProcladius sp.(Unident.)
QDAE1199Chironomidae, Subfam. TanypodinaeAblabesmyia sp.(Unident.)
QDAE1299Chironomidae, Subfam. TanypodinaeParamerina sp.(Unident.)
QDAE1399Chironomidae, Subfam. TanypodinaePentaneura sp.(Unident.)
QDAE1799Chironomidae, Subfam. TanypodinaeLarsia sp.(Unident.)
QDAE99A6Chironomidae, Subfam. TanypodinaePentaneurini ST1
QDAF0399Chironomidae, Subfam. OrthocladiinaeParakiefferiella sp.(Unident.)
QDAF0499Chironomidae, Subfam. OrthocladiinaeNanocladius sp.(Unident.)
QDAF0599Chironomidae, Subfam. OrthocladiinaeStictocladius sp.(Unident.)
QDAF0699Chironomidae, Subfam. OrthocladiinaeCorynoneura sp.(Unident.)
QDAF0799Chironomidae, Subfam. OrthocladiinaeThienemanniella sp.(Unident.)
QDAF1199Chironomidae, Subfam. OrthocladiinaeParametriocnemus sp.(Unident.)
QDAF1399Chironomidae, Subfam. OrthocladiinaeEukiefferiella sp.(Unident.)
QDAF1499Chironomidae, Subfam. OrthocladiinaeCardiocladius sp.(Unident.)
QDAF1599Chironomidae, Subfam. OrthocladiinaeCricotopus sp.(Unident.)
QDAF99A1Chironomidae, Subfam. OrthocladiinaeBotryocladius grapeth
QDAF99A9Chironomidae, Subfam. Orthocladiinae'SO4'
QDAH0399Chironomidae, Subfam. Chironominae, Tribe TanytarsiniCladotanytarsus sp.(Unident.)
QDAH0499Chironomidae, Subfam. Chironominae, Tribe TanytarsiniTanytarsus sp.(Unident.)
QDAH0676Chironomidae, Subfam. Chironominae, Tribe TanytarsiniParatanytarsus/Rheotanytarsus sp.(Unident.)
QDAI0199Chironomidae, Subfam. Chironominae, Tribe ChironominiHarrisius sp.(Unident.)
QDAI0699Chironomidae, Subfam. Chironominae, Tribe ChironominiDicrotendipes sp.(Unident.)
QDAI0799Chironomidae, Subfam. Chironominae, Tribe ChironominiKiefferulus sp.(Unident.)
QDAI0899Chironomidae, Subfam. Chironominae, Tribe ChironominiPolypedilum sp.(Unident.)
QDAI2099Chironomidae, Subfam. Chironominae, Tribe ChironominiDemicryptochironomus sp.(Unident.)
QDAI2499Chironomidae, Subfam. Chironominae, Tribe ChironominiParacladopelma sp.(Unident.)
QE02A199BaetidaeBaetid genus 1 sp.(Unident.)
QE02A299BaetidaeBaetid genus 2 sp.(Unident.)
QE030199OniscigastridaeTasmanophlebia sp.(Unident.)
QE059999ColoburiscidaeColoburiscidae (Unident.)
QE0601A3LeptophlebiidaeAtalophlebia sp.3
QE060299LeptophlebiidaeGarinjuga sp.(Unident.)
QE060899LeptophlebiidaeNousia sp.(Unident.)
QE060999LeptophlebiidaeKoorrnonga sp.(Unident.)
QE061299LeptophlebiidaeUlmerophlebia sp.(Unident.)
QE061399LeptophlebiidaeNeboissophlebia sp.(Unident.)
QE089999CaenidaeCaenidae (Unident.)
QH560199VeliidaeMicrovelia sp.(Unident.)
QH650599CorixidaeMicronecta sp.(Unident.)
QM010199CorydalidaeArchichauliodes sp.(Unident.)
QO120708AeshnidaeAustroaeschna unicornis unicornis
QO160701CorduliidaeEusynthemis brevistyla
QP010101EustheniidaeEusthenia venosa
QP020101AustroperlidaeAcruroperla atra
QP020301AustroperlidaeAustropentura victoria
QP030202GripopterygidaeDinotoperla brevipennis
QP0302A2GripopterygidaeDinotoperla serricauda / thwaitesi
QP030401GripopterygidaeEunotoperla kershawi
QP030599GripopterygidaeIlliesoperla sp.(Unident.)
QP031117GripopterygidaeRiekoperla tuberculata
QP031118GripopterygidaeRiekoperla williamsi
QT0113A3HydrobiosidaeTaschorema complex spp.(Unident.)
QT011405HydrobiosidaeUlmerochorema rubiconum
QT020199GlossosomatidaeAgapetus sp.(Unident.)
QT030599HydroptilidaeHydroptila sp.(Unident.)
QT0602A1HydropsychidaeAsmicridea sp.1
QT060301HydropsychidaeAustropsyche victoriana
QT0605A1HydropsychidaeCheumatopsyche sp.1
QT0605A2HydropsychidaeCheumatopsyche sp.2
QT0605A4HydropsychidaeCheumatopsyche sp.4
QT0605A5HydropsychidaeCheumatopsyche sp.5
QT0608A2HydropsychidaeSmicrophylax sp.2
QT080402EcnomidaeEcnomus continentalis
QT150399ConoesucidaeCostora sp.(Unident.)
QT159998ConoesucidaeLingora/Hampa/Matasia (Unident.)
QT180104CalocidaeCaenota plicata
QT180599CalocidaeTamasia sp.(Unident.)
QT210199PhilorheithridaeAphilorheithrus sp.(Unident.)
QT230101AtriplectididaeAtriplectides dubius
QT240199CalamoceratidaeAnisocentropus sp.(Unident.)
QT250501LeptoceridaeNotalina bifaria
QT250799LeptoceridaeOecetis sp.(Unident.)
QT251104LeptoceridaeTriplectides ciuskus
QT251108LeptoceridaeTriplectides similis


Appendix 3. List of sites where paired sample processing data were available.


Study Code

Stream

Location

CRC FE Sampling Date

EPA Sampling Date

Site Status

AMG

east

AMG

north

Catch. Area

Altitude

D1Dandenong CreekDoongala Forest (Dandenong Ranges NP)20-Apr-9511-Apr-95R3543580971.63260
D1Dandenong CreekDoongala Forest (Dandenong Ranges NP)01-Dec-9418-Nov-94R3543580971.63260
D5Dandenong CreekJells Park (Wheelers Hill)29-Nov-9431-Oct-94T341558042139.9353
D5Dandenong CreekJells Park (Wheelers Hill)12-Apr-9527-Apr-95T341558042139.9353
E5Deep CreekSunbury Rd (Bulla)17-Nov-9414-Nov-94R305858327880.3287
E5Deep CreekSunbury Rd (Bulla)28-Apr-9504-Apr-95R305858327880.3287
G3Gardiners CreekStation St (Box Hill)18-Dec-9518-Dec-95T33445810229.457
G4Gardiners Creekd/s Warrigal Rd (Chadstone)18-Dec-9518-Dec-95T33205806542.8735
G5Gardiners CreekCato St near Toorak Rd (Hawthorn East)18-Dec-9518-Dec-95T327558096100.4117
J3Jacksons CreekVaughan St (Sunbury)16-Nov-9414-Nov-94R300458383345.32186
J3Jacksons CreekVaughan St (Sunbury)16-May-9504-Apr-95R300458383345.32186
M5Merri CreekHeidelberg Rd (Clifton Hill)06-Dec-9421-Oct-94T324258162411.7220
M5Merri CreekHeidelberg Rd (Clifton Hill)27-Apr-9517-Mar-95T324258162411.7220
U5Mullum Mullum CreekWarrandyte Rd (Doncaster East)02-Dec-9420-Oct-94T33925820134.0934
O2Olinda CreekMt Evelyn Recreation Camp24-Nov-9424-Oct-94R35775815522155
O2Olinda CreekMt Evelyn Recreation Camp20-Apr-9502-Mar-95R35775815522155
W5Woori Yallock CreekMacclesfield-WY Rd (Yellingbo)23-Nov-9402-Dec-94R36825813683.01107
W5Woori Yallock CreekMacclesfield-WY Rd (Yellingbo)24-Apr-9510-May-95R36825813683.01107
Y1Yarra RiverWoori Yallock u/s of bridge at next bend01-Dec-9401-Dec-94R3710581881118.7590
Y5Yarra RiverWarrandyte02-Nov-9402-Nov-94T3432582172335.9230
Y5Yarra RiverWarrandyte21-Feb-9521-Feb-95T3432582172335.9230
Y6Yarra RiverFitzsimmons Lne u/s bridge Templestowe02-Nov-9402-Nov-94T3357582112622.1220
Y6Yarra RiverFitzsimmons Lne u/s bridge Templestowe01-Mar-9501-Mar-95T3357582112622.1220
Y7Yarra RiverBanksia St Heidelberg03-Nov-9403-Nov-94T3306581883026.1118
Y7Yarra RiverBanksia St Heidelberg27-Feb-9527-Feb-95T3306581883026.1118


Figures & Tables

List of Figures

  1. MDS ordinations of 25 sites used in the comparison of processing methods, based on lowest taxonomic level resolution, using all taxa. (a) Field-sorted samples, (b) Lab-sorted samples. Four groups of sites (eastern hinterland small streams, western hinterland small streams, Yarra River, and metropolitan sites) are grouped by kernel density contour curves, P=0.9.
  2. MDS ordinations of 25 sites used in the comparison of processing methods based on lowest taxonomic level resolution. Lines join method-pairs: i.e. samples taken from the same site in the same season but processed by different methods (field-sort and lab-sort). Large symbols indicate field-sorted samples. Grouping conventions as in Figure 1. (a) Using all taxa, (b) biased taxa (with fidelity to one method of ³ 1.50, and Oligochaeta) removed, (c) rare taxa (occurring in <10% of samples) removed, (d) biased and rare taxa removed.
  3. MDS ordinations of 25 sites used in the comparison of processing methods based on family-level resolution. Lines join method-pairs: i.e. samples taken from the same site in the same season but processed by different methods (field-sort and lab-sort). Large symbols indicate field-pick samples. Grouping conventions as in Figure 1. (a) Using all families, (b) biased families (with fidelity to one method of ³ 1.50, and Oligochaeta) removed, (c) rare families (occurring in <10% of samples) removed, (d) biased and rare families removed.
  4. Scatter plots showing the relationship between urban model O/E ratios and BOD, Catchment Imperviousness and BODIMP. Enclosed groups of sites are discussed in text.
  5. Relationship between O/E ratios from the EPA Victorian family model and catchment imperviousness for a selection of reference and test sites.

List of Tables

  1. Description of predictor variables used in model construction
  2. Fidelity analysis for twenty-five pairs of samples, each pair taken in the same season from the same site. One of each sample pair was field-sorted, and one was lab-sorted.
  3. Reference site group membership
  4. General physiographic description of model groups
  5. Predictor variables used in particular models
  6. Bands representing different levels of biological condition for the urban models
  7. Test site assessment by urban models and an objective urban impact variable (BODIMP)
  8. Summary of Wilcoxon test results comparing urban model outputs against an objective urban impact variable
  9. Comparison of urban models and the EPA Victorian family model


<TITLE>Urban AUSRIVAS: An evaluation of the use of AUSRIVAS models for urban stream assessment.
</TITLE>
<META NAME="DC.Title" CONTENT="Urban AUSRIVAS: An evaluation of the use of AUSRIVAS models for urban stream assessment.">
<META NAME="DC.Creator" CONTENT="CRC for Freshwater Ecology ">
<META NAME="DC.Type" CONTENT="text">
<META NAME="DC.Date" CONTENT="1999">
<META NAME="DC.Format" CONTENT="text/html">
<META NAME="DC.Coverage" CONTENT="Australia">
<META NAME="DC.Description" CONTENT="The feasibility of developing and using area specific urban AUSRIVAS models was evaluated using existing stream macroinvertebrate datasets for the Melbourne metropolitan and hinterland area. To obtain an adequate number of reference sites for model development a number of datasets had to be combined. Melbourne hinterland sites with predominantly rural landuse were adopted as reference sites for urban areas. Datasets from the CRC for Freshwater Ecology (lab-sorted samples) were combined with the Victorian EPA National River Health Program dataset and the Melbourne Water Corporation Streamwatch dataset (both field-sorted samples). It was determined that lab-sorted and field-sorted datasets had some quantitative differences in the taxa recorded for the same sites. However, despite these systematic differences, multivariate analyses of community composition indicated that the lab-sorted and field-sorted datasets contained similar information about the patterns in macroinvertebrate community composition.">
<META NAME="DC.Relation" CONTENT="This report describes the outcomes of a research project conducted under the Urban Research and Development sub-program of the National River Health Program (NRHP). The NRHP is an on-going national program established in 1993, managed by the Land and Water Resources Research and Development Corporation (LWRRDC) and Environment Australia. Its mission is to improve the management of Australia's rivers and floodplains for their long-term health and ecological sustainability">
<META NAME="DC.Source" CONTENT="Environment Australia community Information Unit">
<META NAME="DC.Subject" CONTENT="Water, rivers">
<META NAME="DC.Publisher" CONTENT="Land and Water Resources Research and Development Corporation. GPO Box 2182 Canberra ACT 2601">
<META NAME="DC.Publisher" CONTENT="Published Electronically on au.riversinfo.org by the Environmental Information Association (Incorporated) with the permission of LWRRDC and Environment Australia.">
<META NAME="DC.Rights" CONTENT="Copyright (©) LWRRDC">
<META NAME="DC.Identifier" CONTENT="Urban AUSRIVAS: An evaluation of the use of AUSRIVAS models for urban stream assessment. CRC for Freshwater Ecology. P. Breen1,2, C. Walsh1,2, S. Nichols3, R. Norris3, L. Metzeling4, &. J. Gooderham2. 1 Melbourne Water Corporation , 2 Water Studies Centre, Monash University , 3 University of Canberra , 4 Environment Protection Authority, Victoria">
<META NAME="DC.Identifier" CONTENT="http://au.riversinfo.org/library/nrhp">
<meta name= "DC.Language" content = "en">

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