Australian River Assessment System Research Articles

What’s wrong with the Australian River Assessment System (AUSRIVAS)?

The Australian River Assessment System (AUSRIVAS or AusRivAS) is a national biomonitoring scheme that supposedly assesses the ‘biological health’ of rivers. AUSRIVAS outputs observed-over-expected (O/E) indices derived from macroinvertebrate survey data obtained both at a site to be assessed and at designated reference sites. However, AUSRIVAS reference sites lack any consistent or quantified status, and, therefore, AUSRIVAS O/E indices have no particular meaning. Moreover, many studies have found AUSRIVAS O/E to be a weak or inconsistent indicator of exposure to anthropogenic or human-influenced stressors. Poor performance by AUSRIVAS may relate to numerous factors including the following: (1) variable reference-site status, (2) inappropriate model predictors, (3) limitations of O/E indices, (4) inconstant sampling methods, and (5) neglect of non-seasonal temporal variability. The indices Ephemeroptera–Plecoptera–Trichoptera (EPT) and stream invertebrate grade number – average level (SIGNAL) provide alternatives that have often outperformed AUSRIVAS O/E in comparative tests. In addition, bioassessment of Australian rivers might be advanced by the development of diagnostic methods to identify the stressors causing ecological impact rather than merely to infer impact intensity and assign quality ratings to assessment sites.

Bayesian reference condition models achieve comparable or superior performance to existing standard techniques

AbstractDespite the existence of many approaches to reference-condition modeling, Bayesian statistical methods have not been used. We assessed whether a hybrid approach that combined features of existing reference-condition approaches with Bayesian model fitting and assessment of test sites could provide superior results to existing established methods. We used 4 Bayesian models of increasing complexity to develop and test reference-condition models for 5 biotic endpoints across 3 data sets. Our best models were comparable or superior to standard approaches (Benthic Assessment of Sediment, Australian River Assessment System) using the same data. Those of our models with the simplest endpoint (species richness) performed best. On average, those models with the simplest model structures also performed best, but differences in performance among models of different complexity were small. All models performed poorly at detecting the lower levels of simulated impact in the test data. However, these impacts were...

Contribution of national bioassessment approaches for assessing ecological water security: an AUSRIVAS case study

River managers in Australia are managing in the face of extremes to provide security of water supply for people, production and the environment. Balancing the water requirements of people, environments and economies requires that water security is viewed holistically, not just in terms of the water available for human consumption. Common definitions of water security focus on the needs of both humans and ecosystems for purposes such as drinking, agriculture and industrial use, and to maintain ecological values. Information about achieving water security for the environment or ecological purposes can be a challenge to interpret because the watering requirements of key ecological processes or assets are not well understood, and the links between ecological and human values are often not obvious to water users. Yet the concepts surrounding river health are inherently linked to holistic concepts of water security. The measurement of aquatic biota provides a valuable tool for managers to understand progress toward achieving ecological water security objectives. This paper provides a comprehensive review of the reference condition approach to river health assessment, using the development of the Australian River Assessment System (AUSRIVAS) as a case study. We make the link between the biological assessment of river health and assessment of ecological water security, and suggest that such an approach provides a way of reporting that is relevant to the contribution made by ecosystems to water security. The reference condition approach, which is the condition representative of minimally disturbed sites organized by selected physical, chemical, and biological characteristics, is most important for assessing ecological water security objectives.

Assessing effects of flow alteration on macroinvertebrate assemblages in Australian dryland rivers

Summary1. Possible impacts of water‐resource development on assemblages of freshwater macroinvertebrates were investigated in the upper Darling River and some of its tributaries in north‐western New South Wales (Australia), an arid and semi‐arid region of low relief where alteration of river flows has intensified through expansion of irrigated agriculture.2. Study sites were grouped into four hydrological regimes resulting from impoundment, flow regulation, water abstraction and natural variation, namely (i) intermittent flow with relatively little hydrological alteration from water‐resource development, (ii) intermittent flow with substantial alteration, (iii) near‐perennial flow with substantial alteration but unimpounded and (iv) near‐perennial flow with substantial alteration plus impoundment by weirs that stabilise water levels.3. Macroinvertebrates were sampled with three methods (a quantitative cylinder sampler, handnet sampling and baited traps) in three periods with differing hydrology (recessional low flow in June 2003, high flow in March 2004 and increasing flow after drought in December 2004).4. Taxonomic richness, assemblage composition and catch per unit effort of the crayfish Cherax destructor differed significantly among the site groups, but total macroinvertebrate density and the AUSRIVAS O/E (Australian River Assessment System observed‐over‐expected) index did not. The principal spatial differences were between the intermittent and near‐perennial rivers, and apparent effects of water‐resource development and impoundment were more subtle. Temporal differences in richness, abundance and composition were substantial and appeared to be related mainly to variations in discharge and temperature.5. Current macroinvertebrate‐based methods for assessing the ‘condition’ or ‘health’ of Australian dryland rivers are inadequate. Such assessments might be improved with (i) reference data that take adequate account of antecedent hydrological conditions, (ii) consideration of long‐term taxonomic richness as well as richness on individual sampling occasions, (iii) evaluation of invertebrate population sizes, (iv) analysis of assemblage data by trait composition and (v) adoption of the genus as the default level of taxonomic resolution.

A diatom species index for bioassessment of Australian rivers

The Diatom Index for Australian Rivers (DIAR), originally developed at the genus level, was reformulated at the species level with data from diatom sampling of rivers in the Australian Capital Territory, New South Wales, Queensland, South Australia and Victoria. The resulting Diatom Species Index for Australian Rivers (DSIAR) was significantly correlated with the ARCE (Assessment of River Condition, Environment) index developed in the Australian National Land and Water Resources Audit (NLWRA), and with nine of the ARCE’s constituent indices and sub-indices, across 395 river reaches in south-eastern Australia. These correlations were generally stronger than those shown by the biological index that was used to assess river condition in the NLWRA, the ARCB (Assessment of River Condition, Biota) index based on macroinvertebrates and the Australian River Assessment System (AUSRIVAS). At a finer spatial scale, DSIAR was strongly and significantly correlated with measures of catchment urbanisation for streams in the eastern suburbs of Melbourne, Victoria. DSIAR scores across south-eastern Australia bore little relationship to the latitude, longitude or altitude of sampling sites, suggesting that DSIAR is not greatly affected by macro-geographical position. In addition, DSIAR scores did not vary greatly among small-scale hydraulic environments within a site. DSIAR appears to have potential as a broad-scale indicator of human influences on Australian rivers, especially the effects of agricultural and urban land use, and also for impact studies at a local scale. Further evaluation is warranted to test the sensitivity of the index to natural variables such as catchment geology, and to assess its performance in northern, western and inland Australia.

Bioassessment in A Harsh Environment: A Comparison of Macroinvertebrate Assemblages at Reference and Assessment Sites in An Australian Inland River System

The Lachlan River system of inland New South Wales, which extends into semi-arid areas, is prone to natural extremes of climate and water quality and has been almost entirely modified since European settlement in Australia. We used this system as a proving ground for the mainly qualitative bioassessment metrics for river macroinvertebrates that are used widely in Australia--the EPT (Ephemeroptera, Plecoptera and Trichoptera) index, the SIGNAL (Stream Invertebrate Grade Number Average Level) biotic index and the AUSRIVAS O/E (Australian River Assessment System Observed over Expected) index--plus a recently developed qualitative index, the observed proportion of potential taxa (OPP). We tested these metrics on their ability to discriminate between sites judged to be less disturbed by human activities (reference sites) and sites selected by a semi-random process and therefore expected to have a higher average level of human disturbance (assessment sites). All metrics except the AUSRIVAS O/E index differed significantly between the two types of sites at higher altitudes, with SIGNAL showing the greatest discrimination. Assessment at these altitudes was more effective if based on composite data from multiple mesohabitats rather than data from single mesohabitats. No metric differentiated the two types of sites in the more arid, lowland, floodplain region of the river system. We suggest that Australia relies too heavily on bioassessment concepts developed to assess water pollution in well-watered regions of the Northern Hemisphere. Effective assessment of human impacts on macroinvertebrates in the rivers of inland Australia requires a better understanding of the roles of flow regimes, including flood and drought sequences, and of microhabitat structure and invasive alien species. Quantitative approaches may also be required.

Evaluation of Four Live-Sorting Methods for Use in Rapid Biological Assessments Using Macroinvertebrates

Rapid bioassessment (RBA) techniques for evaluating river health are now commonplace and there is much debate on the best methods that should be used. One of the important features of RBA is subsampling of large qualitative or semi-quantitative samples to reduce the costs associated with handling and identifying animals. In Australia, the Australian River Assessment System (known as "AusRivAS") has been widely used since 1994 to monitor and assess river health. To test the efficacy of AusRivAS protocols, four live-sorting protocols, the standard Australian River Assessment Scheme (AusRivAS) and three suggested improvements, were evaluated in three habitat types and in clear and turbid rivers. The suggested improvements included using magnification during the live-sort process, separate sorting of coarse and fine fractions and increasing the amount of time or animals collected. There was no statistically significant difference between any of the trialed live-sort protocols in terms of the number of taxa collected compared to the number remaining, the community composition, the abundances of individual families collected, or the AusRivAS Observed/Expected taxa ratios. The lack of differences between the live-sort protocols suggests that technicians using the current standard AusRivAS protocols are able to effectively obtain a representative subsample of animals from the whole kick or sweep net qualitative sample. This has the advantage of cost savings because no retraining will be required, field procedures will remain uncomplicated and previous river health assessments will remain valid.

Optimising the mutual information of ecological data clusters using evolutionary algorithms

The Australian River Assessment System (AusRivAS) is a nation-wide programme designed to assess the health of Australian rivers and streams. In order to produce river health assessments, the AusRivAS method uses the outcomes of cluster analysis applied to macroinvertebrate data from a number of different locations. At present, the clustering step is conducted using the statistical Unweighted Pair Group Arithmetic Averaging (UPGMA) method. A potential shortcoming of this approach is that it uses a linear performance measure for grouping similar data points. A recently developed approach for clustering ecological data (MIR-max) overcomes this limitation by using mutual information as the performance measure. However, MIR-max uses a hill-climbing approach for optimising mutual information, which could become trapped in local optima of the search space. In this paper, the potential of using evolutionary algorithms (EAs), such as genetic algorithms and ant colony optimisation algorithms, for maximising the mutual information of ecological data clusters is investigated. The MIR-max and EA-based approaches are applied to the South Australian combined season riffle AusRivAS data, and the results obtained are compared with those obtained using the UPGMA method. The results indicate that the overall mutual information values of the clusters obtained using MIR-max and the EA-based approaches are significantly higher than those obtained using the UPGMA method, and that the use of genetic and ant colony optimisation algorithms is successful in determining clusters with higher overall mutual information values compared with those obtained using MIR-max for the case study considered.

Biological indicators of stream health using macroinvertebrate assemblage composition: a comparison of sensitivity to an urban gradient

Biological indicators are increasingly being used as integrative measures of ecosystem health in streams, particularly indicators using macroinvertebrate assemblage composition. Several indicators of this type have been advocated, including biotic indices based on taxa sensitivities, richness indices and ratios of observed to expected taxa from models predicting assemblage composition in streams with little human impact (O/E scores). The present study aimed to compare the sensitivity of indicators of each of these types (all used for legislated objectives for stream protection in Victoria, Australia) to a gradient of urban disturbance in 16 streams in a small area in eastern Melbourne. The biotic index SIGNAL and number of Ephemeroptera, Plecoptera or Trichoptera families were the most sensitive indicators, whereas total number of families and O/E scores from Australian river assessment system (AUSRIVAS) models were least sensitive. Differences in sensitivity were not the result of sampling or taxonomic inadequacies. AUSRIVAS and similar models might be improved by using only predictor variables that are not affected by human impacts and by sounder approaches to model selection. Insensitivities of indicators and misclassification of sites by the Victorian objectives show that assessment of indicators against disturbance gradients is critical for setting management objectives based on biological indicators.

A null model for the expected macroinvertebrate assemblage in streams

Predictive models such as River InVertebrate Prediction And Classification System (RIVPACS) and AUStralian RIVer Assessment System (AUSRIVAS) model the natural variation across geographic regions in the occurrences of macroinvertebrate taxa in data from streams that are in reference condition, i.e., minimally altered by human-caused stress. The models predict the expected number of these taxa at any stream site, assuming that site also is in reference condition. A significant difference between the ratio of observed (O) and expected (E) taxa (O/E) and 1.0 indicates that the site is not in reference condition. The standard deviation (SD) of O/E values estimated for a set of reference sites is a measure of predictive-model precision, with a small SD indicating that the model accounts for much of the variability in E that is associated with natural factors such as stream size and elevation. We propose a null model for E that assumes fixed occurrence probabilities for individual taxa across reference sites. The null model explains none of the variability in E caused by natural factors, so the SD of its O/E predictions is the upper limit attainable by any predictive model. We also derive a theoretical lower limit for SD of O/E that is caused only by replicate-sampling variation among predictions from a perfect model. Together, the null-model and replicate-sampling SDs estimate the minimum and maximum precision, respectively, attainable by any predictive model for a given set of reference-site data. A predictive model built from data at 86 reference sites in the Mid-Atlantic Highlands region, USA, had SD = 0.18 for O/E across those sites, while the corresponding null model had SD = 0.20, indicating relatively little gain from the predictive-model effort. In contrast, a model built from 209 sites in North Carolina, USA, had predictive- and null-model SDs of 0.13 and 0.28, respectively, indicating that the North Carolina predictive model had relatively high gain in precision over the null model. Replicate-sampling SDs of O/E for the Mid-Atlantic and North Carolina data were 0.09 and 0.11, respectively, suggesting that the North Carolina predictive model had little room for further improvement, in contrast to the Mid-Atlantic model. The precisions of null-model estimates were lower than those of predictive models, so null models somewhat underestimated the percentages of 447 and 1773 test assemblages from the Mid-Atlantic region and North Carolina, respectively, that differed significantly from reference conditions. The estimates illustrate how a simple and easily built null model provides a lower bound for the prevalence of impaired streams within a region.

ANNA: A new prediction method for bioassessment programs

Summary1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial.2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS.3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r2, intercept and slope. Second, the two models’ assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites.4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS‐ and RIVPACS‐type models, if not to replace them.

Reproducibility of AUSRIVAS rapid bioassessments using macroinvertebrates

AbstractRapid bioassessments of stream health using macroinvertebrates are particularly useful when information is required quickly or when a large number of sites is to be investigated. The purpose of our study was to examine the effect of small-scale temporal variability (weeks) on rapid bioassessments using the New South Wales AUStralian RIVer Assessment System (AUSRIVAS) protocols. AUSRIVAS provides an assessment of stream condition based on the ratio of the number of taxa collected (observed) at a site, and the number predicted (expected) by a multivariate model. Repeated bioassessments were conducted in both riffle and edge habitats at 2 reference condition sites. Mean ratio of observed to expected number of taxa (O/E-Taxa value) was close to 1 for all sites and times, as expected for reference condition sites. O/E-Taxa values did not vary significantly over 4 to 6 wk, except for riffle samples from one site. Collecting 4 replicate samples increased the consistency of the allocation of sites to cate...

New sensitivity grades for Australian river macroinvertebrates

The SIGNAL biotic index for river macroinvertebrates, originally developed and tested in eastern Australia, was revised for application to the entire continent. Macroinvertebrate survey data from the National River Health Program were used to set grade numbers between 1 and 10 to represent the water-quality sensitivities of 210 taxa. Grades were assigned at the taxonomic levels customarily used by government agencies (predominantly family level) and by community groups (mainly order). A new index version using these grades, SIGNAL2, was correlated with water temperature, turbidity, electrical conductivity, alkalinity, pH, dissolved oxygen, total nitrogen and total phosphorus. Because of natural spatial variation in water quality, index scores need to be interpreted in a local context or against site-specific predictions generated by the Australian River Assessment System (AUSRIVAS).

Application of the Australian river bioassessment system (AUSRIVAS) in the Brantas River, East Java, Indonesia

Assessment of river ‘health’ using biological methods, particularly those based on macro-invertebrates, is now commonplace in most developed countries. However, this is not the case in most developing countries, where physical and chemical methods are used to assess water quality, with very little use of biological assessment methods. This paper reports on a project that aimed to assess the possible introduction of biological assessment of river condition using the Australian River Assessment System (AUSRIVAS) into Indonesia. The paper addresses three components of the project: (1) science–does the bioassessment method work in this tropical region? (2) resources–are they adequate and if not what additional resources are needed? (3) politics–what needs to be done to convince the agencies (both central and provincial) to take up such a new philosophy and approach?A pilot study was run in the upper Brantas River, East Java. A total of 66 reference sites and 15 test sites were sampled and the macro-invertebrates collected were identified to family level. A rigorous quality-control protocol was introduced to ensure the data were reliable and reproducible. The macro-invertebrate data were used to develop a predictive model of the AUSRIVAS type for the upper Brantas River, and the model was then used to assess the ‘health’ of sites that were presumed to be damaged in this section of the river. A number of difficulties were experienced during the study, including: locating reference sites sufficiently unmodified by humans; lack of skills to identify animals collected; and a paucity of facilities required for aquatic macro-invertebrate identification (e.g. identification keys and good quality binocular microscopes).For resources, the major constraint to the introduction of a bioassessment capability in Indonesia is the lack of personnel trained in the bioassessment techniques. An ‘on-the-job’ training approach was adopted, largely because of the specialist nature of this work. Six Indonesians were trained and will now become the ‘trainers’ of further Indonesian scientists (we have called this process ‘training-the-trainers’).For politics, it was hoped that the AUSRIVAS method would be suitable for introduction into the Indonesian national Clean River Program. A strategy was developed and implemented to ensure the method and its outputs were accepted technically by the Indonesian scientific community, and also by the resource managers and relevant government officials. Experience shows that if the latter do not see how the bioassessment information will be used for management purposes they will not accept the method even if it is scientifically sound.

Assessment of the biological health of the Brantas River, East Java, Indonesia using the Australian River Assessment System (AUSRIVAS) methodology

This study aimed to evaluate the applicability of the Australian River Assessment System (AUSRIVAS) bioassessment methodology to assess the biological health of streams in the upper-middle Brantas River catchment, East Java, Indonesia. A total of 84 `minimally disturbed' reference sites were selected and sampled for macroinvertebrates in riffle habitats. Sampling of macroinvertebrates and identification to family level was conducted by local biologists following intensive training, and under supervision. A quality control protocol was introduced to ensure the data were reliable and reproducible. A suite of `potential predictor' and `monitoring' environmental variables were also measured at each site. The macroinvertebrate data were used to develop a predictive AUSRIVAS model for the upper-middle Brantas river, and the model was then used to assess the `health' of 15 test sites in the catchment. Bioassessment outputs – Observed (O)/Expected (E) ratios – were found to be broadly related to measures of physical disturbance from land use and riparian degradation. Through the process of local reference site selection and sampling, model development, validation and subsequent use, the Australian AUSRIVAS rapid bioassessment method was assessed as being highly applicable to the upper-middle catchment sections of Indonesian river systems.

Prediction and assessment of local stream habitat features using large‐scale catchment characteristics

1. Knowledge of what a habitat should be like, in the absence of the effects of human activities, is fundamental to local stream habitat assessment. It has been suggested that stream habitats are influenced by large‐scale catchment features. This study aimed to identify these relationships so that local‐scale habitat features could be predicted from larger‐scale characteristics. 2. Fifty‐one reference sites from the Upper Murrumbidgee River catchment, south‐eastern Australia, were classified on the basis of the local features of their stream habitat. Large‐scale variables, namely catchment area, stream length, relief ratio, alkalinity, percentage of volcanic rocks, percentage of metasediments, dominant geology and dominant soil type, provided sufficient information for classifying 69% of reference sites into appropriate reference site groups. 3. A model created using these large‐scale catchment variables was able to predict the local habitat features that were expected (E) to occur at a site in the absence of the effects of human activities. These were compared with observed (O) local habitat features to provide an observed‐to‐expected (O/E) ratio, an assessment score of the habitat at a site. The departure of this ratio from 1 enables identification of those sites that may be impacted. A list of habitat features that are expected at a site can provide targets for habitat restoration or enhancement. 4. For impacted sites, when habitat assessment from the habitat predictive model was compared with biological assessment from the Australian River Assessment System (AUSRIVAS) predictive model, it was possible to identify whether habitat degradation or water quality degradation was the cause of biological impairment. Such assessment may make it possible to identify rehabilitation goals relevant to the biota.